Data Scientist - Senior

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Scala Programming, R Programming, Speech recognition, Large data sets

Job Description

Prudential’s Global Technology team is the spark that ignites the power of Prudential for our customers and employees worldwide. Our organization plays a critical and highly visible role in delivering customer-driven solutions across every area of the company. The Global Technology team is made up of diverse, agile-thinking, and highly-skilled professionals; we use our combined capabilities to enable the organization with innovation, speed, agility, scalability and efficiency. 

The Global Technology team takes great pride in our culture where digital transformation is built into our DNA. When you join the Global Technology organization at Prudential, you’ll unlock a challenging and impactful career – all while growing your skills and advancing your profession at one of the world’s leading financial services institutions.

If your skills reside at the intersection of analytics, technology, business and innovation, then we have a unique and exciting opportunity for you!

Prudential Financial is looking for a Data Scientist to join our diverse team of Engineers, Economists, Computer Scientists, Mathematicians, Physicists, Statisticians and Actuaries tasked with mining our industry-leading internal data to develop new analytics capabilities for our businesses.

The role requires a rare combination of sophisticated analytical expertise; business acumen; strategic mindset; client relationship skills, problem solving; and a passion for generating business impact. This is an exciting opportunity to be a part of a strategic initiative that is evolving and growing over time.

Primary responsibilities for this position include leading, designing, building, testing and presenting forward-thinking, high-impact Data Science projects focused on strategic business initiatives.

Responsibilities:

• Develop and maintain consultative relationships with key business stakeholders
• Identify, source, transform and join public, proprietary and internal data sources
• Model large structured and unstructured data sources (e.g. financial transactional, time-series, text, speech/audio and image)
• Implement advanced statistical methods for prediction and optimization including a wide variety of machine learning technologies (logit, regression, decision trees/forests, boosted models, clustering, etc.) for purposes including explorative analysis, survival analysis, segmentation, prediction and recommendation systems
• Perform analysis and implement solutions that maximize business impact
• Prepare and present written and verbal reports to key stakeholders
• Some domestic travel may be required
• Execute all aspects of an advanced analytical projects with minimal guidance
• Identify analytical solutions for business problems 
   

Qualifications:

• Advanced degree (Masters, Ph.D.) in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial, Data Science, or comparable quantitative disciplines
• Two to three years of industry experience with responsibility for developing advanced quantitative, analytical, statistical solutions
• Hands-on experience applying a wide variety of statistical machine learning techniques to real world problems spanning analysis, predictive modeling and optimization on structured and unstructured data
• Experience using tools such as SQL, SageMaker, Python, R, or equivalent for statistical modeling of large data sets
• Well-developed written and oral communication skills with ability to present complex statistical concepts to non-analytical stakeholders (Excel, Word and PowerPoint are a must)
• Software engineering experience in C/C++/C#/Java/Scala or similar object oriented or functional languages is highly regarded but not a prerequisite
  M.B.A. when combined with advanced quantitative degree is highly regarded but not a prerequisite
• Prior academic or industry research experience is highly regarded but not a prerequisite
• Prior exposure to financial services or insurance industry may be helpful but is not a prerequisite