Climbing the data science ladder in insurance

Article Eleanor Brodie, data science manager, Insurance at LexisNexis Risk Solutions U.K. and Ireland

Competition for good data scientists, especially within the insurance industry, is currently hotter than ever and it is likely that demand will exceed supply for quite some time.

The past few years has generated a big shift in consumer behaviour and insurance providers are looking for new data driven solutions to better understand their customers. There has never been a better time for a woman interested in a career in data science to seek out the growing opportunities that exist within insurance.

Those with the core skills will be able to choose from the employers that offer the best opportunities beyond salary and benefits.  They will want to see how their ambitions for a career in data can be realised, that their job will be fulfilling and at times fun.  Creating the right infrastructure to both attract and retain this talent is therefore becoming a business imperative for insurance providers and data providers such as LexisNexis Risk Solutions, serving the insurance industry.

LexisNexis Risk Solutions employs data scientists across multiple insurance sectors – from looking at the risk of motor policy cancellations to analysing data based on a vehicle’s safety features or precisely mapping flood risk for the property insurance market.

As the insurance industry continues to evolve there is a gradual increase in new data sources available, such as satellite and aerial imagery to assess home and commercial property claims. There is a big interest in gaining access to accurate claims data gathered from across the market to help insurance providers improve the efficacy of pricing, underwriting and claims processing. The more data that comes into the market essentially means more opportunities for data scientists. Our data scientists work with billions of records to solve customer problems. Other companies can be limited in the breadth and depth of their data, but we are able to pull it all together in a commercially viable application.

But as with any relatively new role within the technology industry there are several misconceptions about what a data scientist really does. One of the biggest misconceptions is that big data and analytics will eventually replace human capital; this simply isn’t true. Anyone looking at a potential career as a data scientist should not underestimate the potential that human interaction with the data creates.

Another misunderstanding about data science that we encounter within the insurance sector is that there is a simple formula, where all data is poured into a magic funnel that draws out the desired outcome. Before any predictive model can be built the data needs to be enriched, filtered and structured correctly which is a process that relies heavily on quality data sources and knowledge of modelling.

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Looking to the future

If you are starting out in data science within any business, perhaps as a graduate, check what kind of journey you will be on from day one.  The LexisNexis Risk Solutions Data Science Rotational Program (DSRP) sees graduates from disciplines such as mathematics, statistics, computer science, data science, physics, financial math, actuarial science and engineering join LexisNexis for a two-year cycle through four different teams. This experience provides a robust hands-on journey from data access, data analysis, model building to model implementation.

Alternatively, if you are already within a business and want to move into a data science team, find ways to demonstrate you have a passion for data and a basic understanding of the mathematical concepts behind modelling.

Finally, look for organisations or teams that have female leadership groups that play a key role in attracting women to the business. If you can see that there are women at the top who are encouraging, mentoring and supporting other women in the business to reach their true potential, you will know you are on firm ground.

Part and parcel of nurturing talent is continuous on-the-job learning. We sponsor several additional education programmes and encourage employees to push themselves to achieve more, such as passing their actuarial exams.

My sign off piece of advice to a woman considering data science as a career is to be honest!  Data Science isn’t as sexy as it seems; yes, you can build a lot of cool stuff but to build the cool stuff that actually works in the real world you have to understand the data. It’s a hard-fought battle to properly understand data and build something that effectively better predicts an outcome or deploying automation. You must move from the safety of an R&D environment to a production environment, which can be a scary prospect and a hard road to get all the pieces to align.  If you love digging into the data, analysing it and helping companies find insights or even better, do good for society, then you’ve found the right spot.


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Diversity and data science: The roadmap for bridging the inclusion gap in tech

mind-the-gap-ethnicity-pay-gap-featuredI know first hand that tech has a diversity problem. As a computer science major and a career data scientist with a PhD, I’ve been the only woman in many classrooms and meetings.

My experience is not surprising, unique, or unknown: there is a very public conversation about the lack of diversity in the technology workforce. This is a well-known issue. However, what surprised me as a new hiring manager was how institutional this problem is and how challenging it can be to make progress.

I work for a startup company that was co-founded by a woman. Many of our leadership positions are held by women (Head of Product, Head of Data Science, Chief Customer Officer, etc.). We regularly have conversations about diversity issues and our shared frustrations with our industry. But when I was promoted to head of data science, I quickly learned that the best intentions are not nearly enough to build a diverse team.

I am very familiar with pipeline issues, one of the many reasons offered for lack of diversity: there aren’t enough women qualified for technical roles because they drop out of the pipeline at various stages, from girls who opt out of math classes, to qualified technical college graduates who elect to pursue non-technical careers. I have taken part in unconscious bias training; I’ve attended research talks that show how word choices (or even bulleted lists) in a job ad can either encourage or discourage women and minorities from applying. I’ve been interrupted, had my ideas ignored until they were restated by a male colleague, and I’ve been asked all manner of illegal questions during job interviews. All of this is to say that while I was well aware of the challenges and issues, I was sure that by virtue of being a woman in my new position a pool of highly qualified women and minorities would materialise with very little effort.

There are many ways that a lack of diversity can be reinforced in the hiring process. One inexpensive/quick way to build a team is to rely heavily on referrals, which often serve to reinforce the demographics of the people who are already on the team because people are likely to know other people like themselves. The wording of the job ad can scare away diversity candidates. Having too many requirements can scare away candidates who are too intimidated to apply for jobs where they may not meet 100% of the stated qualifications. There is also a tendency to incorrectly associate skills and experience that are not necessary for a role. For example, there are many successful data scientists who have PhDs in physics, however, a PhD in physics is not required to be a good data scientist.

When I had my first opening, I wrote my ad for a Senior Data Science Manager and waited for all the resumes from highly qualified women and minorities to pour in. And I waited. And waited. Meanwhile, I received many applications from overconfident standup comedians, sandwich delivery drivers, and data science students (my ad suggested minimum qualifications of a PhD or equivalent plus years of work experience). I started working with a recruiter and I was soon interviewing many math and physics PhDs who struggled to communicate clearly and did not bring additional skills to my team. I sought advice from friends, one of whom called me out for my preference for PhDs at all (I wasn’t ready to listen). I talked to colleagues at my company about recruiting strategies.

What finally motivated me to move in a different direction was attending a talk that reinforced the same tired recruiting and team building strategies that have been shown to be problematic, leading to hiring the same non-diverse workforce our industry has been hiring for decades. It finally clicked that instead of accepting the status quo and letting myself off the hook because recruiting a diverse team is hard (it is), I needed to take some bold steps. I rewrote my ad and reset the level (from senior manager) to encourage candidates of all levels to apply. This required that I be creative in envisioning how a more junior candidate could contribute to the larger team. I changed recruiters to one that was on board with my hiring objectives, and who committed to sending me resumes from more diverse candidates. Once we opened the recruiting funnel, we started to see many great candidates who had very different backgrounds. We were excited about what many of these candidates could contribute to the team.

My team and I have not “solved” broad diversity problems, but we have moved in a positive direction. While we were optimising our candidate pool to better reflect the population, we also increased the quality of our candidates. For each open position, we had more, highly qualified finalists than we had before.

About the author

Rhona TextorRhonda Textor, joined True Fit as its Head of Data Science in 2015, having pivoted to retail tech from her role in handling applied machine learning for national security at Microsoft.

Rhonda, who leads a team of 7, has been central to the management and modelling of the data that built True Fit’s Fashion Genome and its product roadmap, which supports 17,000 retail brands and processes data from 150million shoppers who are registered users.  This data is then used to help shoppers find clothes and shoes they love and keep, helping retailers close the ‘loyalty loop’ and in turn, retain customers to improve customer lifetime value.

Rhonda has been instrumental in the development of 2 new products during her tenure at True Fit, the latest of which will be launched in 2021 and will combine the data power of the Genome with visual search and outfitting capabilities, in an industry first.


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Why now is the time to get into data science

Boris Paillard, CEO, Le Wagon

female data scientist, woman leading teamDigitisation has affected virtually every company, in every sector and across every department. Processes are being automated, new insights into operations are being generated and new services are being created.

The improvements and efficiencies that this digitisation is generating are not simply a result of ‘better software’, but based on organisations’ ability to collate, analyse and manipulate massive volumes of data. The sheer scale of the data presents challenges as well as opportunities however.

Not only is it the case that the bigger the company, the bigger and more complex the data – it is also the case that the discipline of data science has not yet reached the well-documented, well-known and well-established processes and best-practices that we see in the software space. This is new ground for every organisation and the simple fact is that there is a lag on the skills education and training front when it comes to data science.

This is where we are seeing a shift in the ecosystem around data science. There is far greater understanding of the need to train people in how to apply data science skills to different departments, and how we can retrain people to meet the exploding demand for these skills.

One of the key issues now being addressed directly is the need for diversity in data science teams. Although there is explosive demand for data science skills, women occupy only a minority of these positions - in the UK, women represent less than 17% of the tech workforce.

Redressing this imbalance is crucial to building the value and validity of a field that is seeking to analyse and influence the lives of everyone. As such, it is a crucial moment for women to consider changing their career or to gain new skills that will help them make a bigger impact in their current role. There are huge opportunities, but it can also be somewhat boggling. For people that are interested in data science it’s important to understand that data science is a broad church.

My advice to people is that, there are lots of resources out there, but the best thing you can do is to start playing around with data, not only to experiment and get your head around the principles, but also to gain a better understanding of your own personal skills and objectives.

After that, there are a number of organisations you can work with to gain more formal education – including Udemy or Le Wagon and Imperial College London’s joint Imperial Data Science Intensive Course. But it is only by getting your hands dirty that you will find the right course and pathway into data science for you.

Boris PaillardAbout the author

After studying engineering and applied mathematics at Ecole Centrale Paris, Boris Paillard worked 3 years in investment banking. Passionate about tech & education, he quit his job to work on various tech products before founding Le Wagon to teach tech skills to creative people. For the past 7 years, he has been leading the development of Le Wagon's training programmes and platforms. To date, his teams have trained 10,000+ alumni in Web Development and Data Science across 41 cities, making Le Wagon the world’s leading coding bootcamp worldwide.

 


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Why are we seeing a widening gender pay gap in Data Science?  

Article by Talitha Boitel-Gill, Associate Director at Harnham

mind-the-gap-ethnicity-pay-gap-featuredAccording to the most recent figures provided by the ONS, the gender pay gap in the UK currently stands at 8.9 per cent. Sadly, this figure is relatively unchanged since 2018, and is a menial decrease from 2012’s figure of 9.5 per cent.  

The worst industry offenders are Skilled Trades (22.4 per cent), Process and Plant Machine Operatives (18.1 per cent) and Senior Business Officials (15.9 per cent).

Across the whole jobs market, on average, a woman aged 30-39 earns £16.13 an hour compared to the £17.85 of her male counterpart, a huge gap of 10.68 per cent.

While there are many suggestions as to why women earn significantly less than men, such as having children or needing to take up caring responsibilities, the truth is that the problem lies with employers at the very start of an individual’s career ladder.

Many women are at a disadvantage from graduate level and therefore, no matter how hard they work or how quickly they climb the ranks, they will never be able to match their male counterparts.

How does Data Science fair? 

The number of women in Data Science has always been low in comparison to men; we know that only 25 per cent of the industry was occupied by females last year.

However, the data from Harnham’s most recent Diversity in Data and Analytics report found that this year that number has increased significantly to 30 per cent, pushing the industry closer to that desirable 50/50 split.

Nevertheless, many of these roles are held at entry or mid-level, with very minimal female representation at senior or board level. This means that despite less gender parity, the gender pay gap has widened once again.

The gap is 3.2 per cent bigger than last year, now standing at 10.5 per cent. Despite still being lower than it was in 2018 (13.3 per cent), it is still significantly above the national average. This is an undesirable place for the industry to be.

Currently in Data Science, for every £1 a man earns, a woman only earns 89p. And akin to the issue nationally, this gap starts from the word go, with women earning less than their male counterparts from entry-level and beyond.

What can Data Science (and the rest of the business world) do to combat the gender pay gap? 

Better representation at senior level 

Whilst progress is being made in the Data Science, and the overall Tech, sphere to encourage more women to join the profession, we are still seeing far too many women at entry level, and too few at senior and/or board level.

Only one in five leadership positions are held by women. Despite this number rising slowly but surely over the years, 12 per cent of respondents reported no women in leadership at their place of work, while 88 per cent had fewer female leadership team members than males.

Not only does this undo the current efforts of gender diversity, it also means that many women who may want to enter the market could be put off by the overruling number of senior men.

Additionally, a lack of diversity at the top may ultimately lead to a lack of diversity further down which can have detrimental impacts on a business’ success.

And so, ensuring that there is a clear career path with an equivalent number of opportunities for women and men alike is crucial.

Further encouragement at education level  

Tech, Data and STEM are all very male-dominant sectors, even at educational level. At A-Level, less than a third of female pupils ranked a STEM-related subject first for enjoyment compared to over half of men.

For many, this stark difference is heavily stigma-based. With girls opting for apparently ‘softer’ subjects such as English, Biology or Psychology.

To battle this, we should be shouting about the incredible opportunities STEM and Data could open for our future generation of women, and the influence they could have to quite literally change the world.

This could be through encouragement to attend ‘Women in Tech’ events or learning about inspirational STEM role models, such as Dame Stephanie Shirley or Ada Lovelace, both true pioneers.

Lovelace’s mathematical genius was the brains behind Charles Babbage’s mechanical general-purpose computer. Dame Shirley, a Kindertransport refugee who fled Germany in the Second World War, founded software company Freelance Programmers - the first predominantly female data and programming business.

It's crucial that we are showing our next generation of women that females in this space are celebrates and championed, breaking down the gender-sensitive stereotypes that are still ever-present.

Stand up and stand out 

Whether you’re a senior member of the board or a graduate level employee, now is the time to challenge the gender pay gap. If you know your business is not representative or equal, it’s time to act.

For employees feeling unable to go straight to their employer, find an anonymous way of reporting this to HR if possible. Words without actions are empty, and now is time to fight against a truly needless inequality.

For the full UK Diversity report from Harnham, please click here: https://www.harnham.com/harnham-data-analytics-diversity-report-2021 

About the author

Talitha Boitel-GillTalitha joined Harnham in 2012 following a degree in Politics and American Studies. Since then, she has progressed through the business to the level of Associate Director and now leads the UK Marketing & Insight and Digital Analytics teams.


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Good data science requires diverse data scientists

Article by Justine O’Neill, director, Analytic Partners

female data scientist, woman leading teamAsk someone to picture a data scientist and what do you think they are most likely to conjure in their minds?

Somewhat depressingly, I’d hazard a guess that they would imagine a man, and quite possibly a ‘geeky’ man. In some ways they wouldn’t be wrong – most data scientists are men.

According to the Boston Consulting Group’s (BCG) research carried out earlier this year, only 15% – 22% of data scientists are women. It doesn’t have to be this way, but it will take concerted effort on many people’s part to change it. About 55% of university graduates are women, but from that point onwards the funnel narrows. Only about 35% of STEM degrees are held by women and that drops in the workplace with around 25% STEM jobs being done by women.

So, how to unpick this challenge? Firstly, there is a distinct image problem for data science, algorithms and artificial intelligence (AI) in general. There has been plenty of hype around AI and how it will quickly answer so many of society’s problems and automate mundane and labour-intensive roles – but the media is also full of stories about its limitations and the problems that have arisen when too much weight is placed on algorithms at the expense of human insight.

Most recently we’ve had Ofqual’s disastrous algorithm for A level and GCSE exam results which tipped schools and universities into chaos and was met with derision from teachers and students alike before the government was forced to ditch it entirely. Oh, the irony as we try to make a case for improving data science’s image and appeal among this cohort of students.

But the other challenge centres on data science’s lack of appeal for women specifically. This seems to be partly because when assessing career options, female STEM students are looking for applied, impact-driven work – they want their jobs to have a tangible effect and don’t see data science as fulfilling that.

There is clearly a job to be done among all businesses looking to hire graduates to explain more clearly how data science solves business problems – to promote its demonstrable attributes. Everyone working in the industry should share their inspiring stories about the rigours and rewards that come from their jobs. Students want to hear specifics and get to grips with what the day-to-day expectations and experiences of this job would be – show why it’s not just the domain of the nerds.

Diversity in our sector is imperative. As the author Margaret Heffernan says, “algorithms are opinions encoded in numbers” – we need the broadest range of voices building and working on those algorithms to be alert to the bias that can be built into the data sets used to create them. If your team has genuine breadth of thought and experience, then it is more likely to identify biases and produce more accurate and balanced results.

The business case could not be clearer – ensuring a company has diverse teams is not just because gender balance is a ‘good to have’, it’s essential for strategy and success. It is why men should be championing diversity with the same enthusiasm as women.

No one says this is easy. It may require a root and branch rethink of how your organisation fills its roles. I suspect many people involved in hiring data scientists will bemoan the disproportionate number of men applying to women for every role. But even from this starting position, businesses can successfully achieve a better balance in their workforce.

Everyone needs to look at the wording of their job ads, the tone of voice used and where they are placing their adverts. If you use recruiters have you explicitly requested more diverse longlists of candidates? Look at who internally is involved in the interviewing; changes can be made at all points to help nudge toward a more balanced workforce.

I work for a global analytics consultancy where three of our senior team are women, starting with our president and CEO. This is not the case for many of our competitors, but it does show how diversity can be possible.

A shift is taking place – clients want to work with more diverse agency teams, conferences want better balanced panels and speakers, younger candidates want to be in organisations that better reflect the world outside of work. The business and moral argument are aligning, and everyone needs to get on board.


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Women in tech - the why, what and how of building a career in data science

binary code, data scientist

By Joanna Hu, Principal Data Scientist, Exabeam

With a growing number of organisations recognising the financial, social and cultural benefits of recruiting more women into data science, isn’t it time to explore the opportunities on offer?

Like many women who graduate with a tech degree, it took me a couple of years to figure out that data science was my niche. Thankfully, I eventually found my way and went on to forge a rewarding career in this exciting field.

With advancements like machine learning and big data now in the frame, I’ve been lucky enough to contribute to discoveries and solve real-world problems in healthcare, energy, and now – as principal data scientist at Exabeam – the cybersecurity industry.

I’m not alone in thinking that data science is a rewarding field to work in. Based on overall job satisfaction scores, the role of data scientist is ranked #7 in the Glassdoor ’25 best jobs in the UK for 2019’ listing – with an average base salary of £46K.

A long heritage

Historically, women have made a significant contribution to the evolution of computer science.  Before the invention of electronic computers, women were more prominent in the computer science field, and contributed a lot to the invention of the first electronic computers.  As well as Joan Clarke, who worked alongside Alan Turing to crack the Enigma cyphers during WW2, the other female codebreakers at Bletchley included Margaret Rock, Mavis Lever and Ruth Briggs.

More recently, there’s been trailblazers like Dame Steve Shirley, who first embarked on a technical career at the prestigious Post Office Research Station in Dollis Hill, where the Colossus codebreaking computers used at Bletchley were created. Founding her own software company in 1962, her team of female freelancers would go on to undertake many cutting-edge projects – including programming the black box flight computer used in Concorde.

Today, a new generation of women are forging their futures within the tech sector. Coming from a diversity of backgrounds, they’re making great strides in the field of data science – and many have done so without an initial background in science, technology, engineering or mathematics (STEM).

A field rich with opportunities

Make no mistake, data scientists are in high demand. A recent study found that 80 per cent of UK businesses are looking to hire a data scientist in 2019, and IBM estimates that by 2020 the demand for data scientists and analysts will leap by 28 per cent.

That said, while women represent 47 per cent of the UK workforce, they only hold around 19 percent of all available tech jobs. Clearly, it’s time to redress the balance.

That’s certainly the opinion of bodies like the Alan Turing Institute and organisations like the International Women’s Day (IWD) movement. Indeed, the IWD #BalanceforBetter 2019 campaign is making great strides in changing hearts and minds – by showcasing how women in tech are achieving impressive outcomes for themselves and others.

The good news is a growing number of companies now acknowledge there are significant gains to be won by addressing the issue of gender inequality in their tech workforces. As a result, they’re eager to hire more female data scientists. Indeed, Gartner projects that in the next three years, both women and men will equally populate the role of chief data officer (CDO).

Why companies want more women in data jobs

Research organisations like McKinsey have found that highly diverse companies are 15 percent more likely to outperform those that are not gender diverse. Alongside enhanced financial performance, reports by analysts such as Morgan Stanley, McKinsey and Gartner confirm that having more women in the tech workforce creates a more cooperative and collaborative atmosphere.

Their research findings also highlight how women are more aware of risk, which in the field of big data is a major plus. What’s more, women tend to excel at communication, team nurturing and problem-solving—all vital qualities when working in the field of data, where outcomes depend on asking the right questions, and listening to the answers.

Finally, and perhaps most interestingly, the research findings illustrate how women are strong advocates for data-driven decisions and tend to be more solution-oriented than male counterparts.

I’m not a rocket scientist – can I make it in data science?

Absolutely. If you’re a curious person, are passionate about innovation, and have an interest in technology, then this may well be the career for you. Stephanie Glen’s recent blog – charting her life-changing journey from office cleaner to data scientist – highlights that as far as she’s concerned, a love of logic problems is the most important pre-requisite for the job.

Typically, the skill sets required include math, statistics, coding and system design. But, as a recent article in CIO magazine highlights, exacting true business value from data requires a unique combination of skills that includes storytelling and intuition.

Truth is, women with a passion for learning who want to try something new will find there’s a number of big-name tech companies out there that only too ready to help you develop the digital skills you need to embark on a career in data science. Plus, there are organisations like Girl Geeks that are proactively supporting women to enter and progress in the field.

Top tips?

If you’re already working in the tech field, or are ‘data science’ curious, then teach yourself the data science knowledge and network as much as you can.  Before deciding this was the path I wanted to commit to, I spent time talking to people about their work, went on workshops, joined weekend meetups and tried out small projects from the online courses.

These days, there are lots of resources available to women who want to make a go at it in this field. Find out about which new tools you’ll need to learn, then use your free time to hone your skills – pretty soon, you’ll become an expert.

When it comes to seeking out new job opportunities, follow good companies and people rather than high salaries. Ideally, you’ll want to work for companies that have intelligent leaders and care about their female talent. Most importantly, hunt down a great mentor and commit to continuously learning from superiors and peers.

Finally, believe in yourself and, no matter what roadblocks you face on the journey, don’t let anyone limit your potential.

Joanna HuAbout the author

Joanna has rich industrial working experience within data mining and big data analysis for healthcare institutions, energy companies, and retailers. Through her work she aims to help them identify frauds, predict risk and outcome, reduce cost, and estimate product qualities.

Joanna has a Ph.D. from University of California, Berkeley, in Nanotechnology and a Ph.D. from University of Michigan in computational earth sciences. Before joining Exabeam in 2015 as a senior data scientist she worked at Ayasdi as a data scientist building and improving algorithms for client healthcare institutions to produce the best treatments for patients. Since October 2018 Joanna has been principal data scientist at Exabeam.