Job Description

Job Description

Title: Senior Data Scientist

Location: Remote (EST)

Duration: 04 Months

Job Description:

As a Senior Data Scientist on Institutional Retirement Strategies, you will partner with 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! In addition to applied experience, you will bring excellent problem solving, communication and teamwork skills, along with agile ways of working, strong business insight, an inclusive leadership demeanor and a continuous learning focus to all that you do.

Here is what you can expect in a typical day:

• Responsible for the hands-on development of advanced data science solutions comprising the portfolio developed by the Lead Data Scientist and the technical requirements specified by the Lead Data Scientist. Perform hands-on data analysis, model development, model training, and model testing.
• Write production-level code and partner with machine learning engineers to push development code into production.
• Continuously research new methods for problem solution, including new algorithms, modeling techniques, and data analytics techniques.
• Partner with machine learning engineers to productionize machine learning models. Partner with data engineers to build data pipelines. Partner with software engineers to integrate solutions with business platforms.

The Skills and expertise you bring:

• Advanced degree (Masters, Ph.D.) in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial, Data Science, or comparable quantitative disciplines.
• Working on complex problems in which analysis of situations or data requires an in-depth evaluation of various factors. Exercises judgment within broadly defined practices and policies in selecting methods, techniques and evaluation criteria for obtaining results.
• Ability to learn new skills and knowledge on an ongoing basis through self-initiative and seeking challenges.
• Excellent problem solving, communication and collaboration skills.

Applied experience with several of the following:

• Machine Learning: Understanding of machine learning theory, including the mathematics underlying machine learning algorithms. Expertise in the application of machine learning theory to building, training, testing, interpreting and monitoring machine learning models.
• Generative AI & Natural Language Processing: Experience with modeling and interpreting text analysis including NLP, LLMs (BERT, etc), and Generative AI. Experience in modern Gen AI technologies including RAG, LangChain, LangGraph, vector DB and their application in Institutional Retirement Strategies area.
• Statistics and Computing: Exceptional understanding of: Multivariable Calculus, Linear Algebra, Differential Equations, Applied Probability, Applied Statistics, Computer Science (Programming Methodologies), and Cloud. Knowledge of statistical techniques such as the use of descriptive, inferential, Bayesian statistics, time series analysis etc. to extract business insights and experimentation to solve business problems.
• Data Acquisition and Transformation: Acquiring data from disparate data sources using API's and SQL. Transform data using SQL and Python. Visualizing data using a diverse tool set including but not limited to Python.
• Database Management System: Knowledge of how databases are structured and function in order to use them efficiently. May include multiple data environments, cloud/AWS, primary and foreign key relationships, table design, database schemas, etc.
• Data Wrangling: Preparing data for further analysis; Redefining and mapping raw data to generate insights; Processing of large datasets (structured, unstructured).
• AWS DevOps: Experience in the project development life cycle in an AWS environment. Familiar with development, QA, staging and production deployment stages.
• Programming Languages: Python, SQL.

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