Data Scientist

Position Summary

Job Location: 100 Avenue of the Americas, New York, NY 10013

Note: Company "Hybrid" work attendance policy: In-office work attendance required at the aforementioned office address for collaboration days based on each team's requirement; telecommuting / working from home is permissible for remainder of the same month.

Duties: Design, implement and evaluate statistical models of Real Estate trends and valuations to support a Data Science focused Private Equity Real Estate investment business. Analyze and evaluate large commercial datasets related to economic activity, financial markets, logistics, demographics, salaries, and consumption; cleaning, processing, and transforming raw data into features/variables that support decision-making and model development. Implement non-linear machine learning (ML) algorithms that can leverage statistical correlations within these datasets to produce insights related to Real Estate income and valuations (rent and asset pricing). Perform statistical evaluation of these models, designing metrics and KPIs, as well as contextualizing results for business stakeholders; in particular, develop robust backtesting routines for time series forecasting models to reliably evaluate predictive performance. Conduct systematic R&D experiments to test different ML algorithms, statistical techniques, and feature sets, documenting results, comparing and identifying the best performing solutions. Develop new algorithms and numerical techniques as required to tackle complex problems without readily available solutions. Implement modeling and feature engineering pipelines using state-of-the-art production-level coding techniques, operating in a big data environment in the public cloud using tools such as Python, SQL, Git version control, and command line scripts. Produce analytic outputs such as slides, dashboards, reports, and worksheets to communicate insights and results to external/internal, technical/non-technical stakeholders. Create graphs, charts and visuals to enhance content and present more effectively to stakeholders.

Minimum education and experience required: PhD degree or equivalent in Finance, Economics, Statistics, Mathematical Social Sciences, or related field; OR Master's degree or equivalent in Finance, Economics, Statistics, Mathematical Social Sciences, or related field plus 3 years of experience in Data Science or related experience.

Skills required: Must have experience with machine learning and statistical models including gradient boosting algorithms, tree-based algorithms, linear regression, quantile regression, and unsupervised clustering. Must have experience with time series forecasting including stationarity, trends, seasonality, and backtesting routines. Must have experience with statistical modeling and inference including linear regression coefficient interpretation, and statistical significance tests. Must have experience with systematic modeling R&D including evaluation metric design, experiment tracking, results documentation, performance comparison and model selection. Must have experience with data and feature engineering including cleaning, missing value imputation, aggregation, mathematical transforms, time series transforms (rolling averages and calendar features), normalization and standardization. Must have experience with programming tools for data science including Python, SQL, Git version control, and Command Line Interface (CLI) tools. Must have experience with data visualization including Python libraries such as Matplotlib, Seaborn, and Plotly. Must have experience with analytic products and outputs including slide decks, reports, and worksheets. Must pass company's required skills assessment. Employer will accept any amount of graduate coursework, graduate research experience or experience with the required skills.

The base pay for this role will be between $165,000 and $325,000 per year. This role may also be eligible for other forms of compensation and benefits, such as a discretionary bonus, health, dental and other wellness plans and 401(k) contributions. Discretionary bonus can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.

#LI-DNI