Director Advanced Analytics

Boston, Massachusetts
US$150000 - US$175000 per year

This vacancy has now expired. Please see similar roles below...

Director, Advanced Analytics
Insurance
Greater Boston Area
$150,000 - $175,000

Do you want to join a globally renowned business who promote exploratory analysis and pushing boundaries with data? If you want to join a rapidly growing Analytics Center of Excellence, working on advanced customer focused projects using open source technologies, then this is the role for you. A rapidly growing global insurance company are looking for an advanced analytics professional with proven experience in being both coach and player to help them fully optimize and utilize their omnichannel data to its full potential, delivering insights to diverse stakeholders across the group.

THE ROLE - DIRECTOR, ADVANCED ANALYTICS

This is a unique opportunity for an experienced senior advanced analytics professional to join a rapidly growing, advanced team specializing in predictive modelling and advanced customer focused projects across multiple channels to really make their mark within this Insurance company. As the Director of Advanced Analytics, you will be:

  • Actively involved managing a high-performing team, responsible for deep dive analysis using R, SQL and python for both the online and offline side of the business to understand customer behaviors and trends, as well as tracking customer channel traffic
  • Using your R and SQL/Python skills you will be actively involved in building and developing the strategy around predictive analytics from day one, helping the business utilize and optimize all marketing and customer data for more efficient and effective targeting. You will be leading the team to build out machine learning algorithms and recommendations online to upsell/cross sell products and understand the value to the business.
  • You will be seen as a voice of influence in the business taking full autonomy for your analysis, and delivering insights and recommendations to senior management as well as non-technical audiences. You must be a critical thinker, and able to apply complex analysis to wider business questions

YOUR SKILLS AND EXPERIENCE:

  • Proven experience in a marketing or customer analytics environment, preferably within the financial services or insurance sectors
  • Strong analytics capabilities in R/SAS and SQL/Python with the ability to build predictive models essential.
  • Ability to think critically as well as deliver key insights and recommendations to senior stakeholders and well as strategize collaboratively with a multi-disciplined team
  • Data driven with proven capabilities in growing and managing multi-disciplinary teams

BENEFITS:

As a Director of Advanced Analytics, you can expect to earn up to $175,000 (depending on experience) + highly competitive benefits

HOW TO APPLY?:

Please register your interest by sending your Resume to Jenni Kavanagh via the Apply link on this page

KEYWORDS:

SAS, SQL, R, Python, Predictive Modelling, Regression, Statistics, Google Analytics, Excel, Marketing, Analysis, Customer Insight, Digital, eCommerce, Stakeholder Management, Strategy, ROI, Campaigns, Direct Marketing, Online, Tableau, Optimization, Segmentation, Modeling, Advanced Analytics, Machine Learning, Recommendation Models, Insurance, Financial Service, Data Mining, Predictive Analytics

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55864/JK
Boston, Massachusetts
US$150000 - US$175000 per year

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How to lead a Data team

How To Lead A Data Team

Dream teams from sports to business are an ideal everyone aspires to live up to. But what is it every basketball or football dynasty has which makes them a dream team? What is it that brings individuals together to overcome odds, set examples, find solutions, and create the next best thing? Good management.  The need for good management is no different in the Data Science world. Yet according to our latest Salary Guide, poor management is one of the top five reasons Data professionals leave companies. So, let’s take a look at what poor management is, what causes it, and how businesses can better retain Data talent. What’s Your Data Science Strategy? Most businesses know they need a Data team. They may also assume that a Data Scientist who performed well can lead a Data team. But that isn’t necessarily the case. Managers have to know things like P&L statements, how to build a business case, make market assessments, and how to deal with people. And that’s just for a start.  The leader of a Data team has a number of other factors to consider as well such as Data Governance, MDM, compliance, legal issues around the use of algorithms, and the list goes on. At the same time, they also need to be managing their team with trust, authenticity, and candor. The list of responsibilities can be daunting and if someone is given too much too soon and without support, it can be a recipe for disaster. Other businesses might believe that a top performing Data Scientist would make a good manager. Yet these are two different fields. Or you might look at it this way. If you are willing to upskill a top performing Data professional and train them in managerial skills, giving them the education and support they need, that is one solution. Another solution is to create a Data Science strategy which brings in people with business backgrounds. Data Science is a diverse field and people come from a number of backgrounds not just Computer Science or Biostatistics, for example.  Now that you’ve seen what might cause a manager to fail, let’s take a look at a few tips to help you succeed. Seven Tips for Managing a Data Team Managing a team is about being able to hire, retain, and develop great talent. But if the manager has no management training, well, that’s how things tend to fall apart. Here a few tips to consider to help ensure you and your team work together to become the dream team of your organization: Build trust by caring about your team. Help define their role within the organization. Ensure projects are exciting and that they’re not being asked to do project with vague guidelines or unrealistic timeframes.Be open and candid. Remember, Data Scientists are trained in how to gather, collect, and analyze information. If anyone can see right through a façade, it will be these Data professionals. Have those “tough” conversations throughout every stage of the hiring, onboarding, and day-to-day, so that no one is caught unaware.Offer consistent feedback. And ask for it for yourself as well from your team.Ensure your team understands the business goals behind their projects. Let them in on the bigger picture. Think long-term recruitment for a permanent role, not short-term. If you have an urgent project, consider contracting it out. Prioritize diversity to include academic discipline and professional experience. Does the way this person view the world expand the knowledge of your team’s knowledge? Dream teams don’t always have to agree. Sometimes, the best solutions are found when there are other opinions. Finding the perfect, “Full Stack” Data Scientist or Data Engineer or Analyst is not impossible, and retaining them can be even easier. If you’ve done your job well, your team will trust you, have a balanced skillset, and understand how their work supports the organization and its goals. For more information on how to be a great manager, check out this article from HBR.  Ready for the next step?  Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Battle Royale: Computational Biologists Vs Machine Learning Engineers

Battle Royale: Computational Biologists vs Machine Learning Engineers

From the first genome sequencing in the second revolution to Life Science Analytics as a growing field in the fourth industrial revolution, change has been both welcomed and fraught with fear. Everyone worries about robots, Artificial Intelligence, and in some cases even professionals who have stayed current by keeping up-to-date with trends. And it’s beginning to affect not only “office politics” within the tech space, but even interviewer and interviewee relationships. We’ve seen a growing trend of apprehension between Computational Biologists and Machine Learning Engineers. What could be the cause? Aren’t they each working toward a common goal? It seems the answer isn’t quite so cut and dry as we’d like it to be. Here are some thoughts on what could be driving this animosity. But first, a bit of background. So, What’s the Difference? Computational Biology and Machine Learning are two sides of the same coin; one sets the framework and the other applies what’s been learned. 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