Data Scientist

Hybrid (NYC)

3-Month Contract

$75-85/hr

Role Overview

We are partnered with wealth management firm that specializes in delivering tailored wealth management, financial planning, and investment services to high-net-worth individuals, families, and businesses. The firm focuses on providing comprehensive solutions to help clients manage and grow their wealth, plan for retirement, and navigate complex financial situations.

Responsibilities

• Partner with Product, Engineering, and/or Design to enable clear, impactful, actionable, and innovative insights that shape key decisions.
• Conduct rigorous, high-quality analyses that shape the way they think about the future.
• Define and evaluate key metrics and understand their levers.
• Leverage clear communication skills, with an aptitude for translating analysis results into compelling and comprehensible concrete recommendations for the business.
• Adapt to a rapid changing environment and be proactively inquisitive to identify new areas of opportunity for Data Science.
• Have an eye for scale and identify ways in which your work can have broader impact.
• Have a strong sense of responsibility and integrity and can balance both short-term and long term objectives.

Qualifications

• 3+ years of experience in Data Science & Analytics (prior experience in a start-up, fintech, or social media companies in a product/go-to-market capacity is a plus).
• SQL - you should be fluent with UDFs, joins, aggregation functions, and window functions to build complex queries for analyses.
• Prior experience with Python/R.
• Experience with visualization tools like Tableau, Looker, Amplitude
• Experience with ETL frameworks like Airflow & Stitch.
• Advanced degree in a quantitative field or equivalent experience in one or more of the following: Mathematics, Statistics, Economics, Computer Science, Machine Learning, Data Science, Engineering, etc.
• Strong knowledge of Statistics, Experiments (e.g. A/B Testing), Hypothesis testing, Machine Learning, and other Analytics approaches.
• Bonus:
• Technical chops w/ Git, Terminal, CLI, etc.
• Experience in: Causal Inference, Experiments + Variance Reduction (e.g. Delta Method, Post Stratification, CUPED), Machine Learning (Regression, Classification, Clustering, SVD/PCA, etc.), & Behavioral Econ.