Data Scientist

About the role

About The Role

The Data Scientist will turn large, complex datasets into models, experiments, and decision systems that shape product strategy and improve customer outcomes. The role spans exploratory analysis, causal inference, predictive modeling, and production analytics across behavioral and operational data.

The team partners closely with product managers, software engineers, and ML engineers to define measurable objectives, build reliable data pipelines, and deploy insights into products and business processes. This role is based in New York, NY and is open to remote candidates.

Key Responsibilities

Design and analyze experiments, including A/B tests and quasi-experimental studies, using Python, SQL, and statistical inference to measure product and model impact

Build and validate predictive models for classification, forecasting, ranking, and customer behavior using scikit-learn, XGBoost, PyTorch, or comparable frameworks

Develop reusable data workflows and analytical datasets with SQL, dbt, and Spark across cloud-based data platforms such as Snowflake, BigQuery, or Databricks

Translate ambiguous product and business questions into clear metrics, hypotheses, analytical plans, and recommendations for technical and non-technical stakeholders

Partner with ML engineers to productionize models, define monitoring requirements, and track accuracy, drift, bias, and business performance after deployment

Create dashboards, reports, and self-service analytical tools using Looker, Tableau, or similar platforms to make key metrics accessible across the organization

Document methodologies, assumptions, limitations, and results; review analytical work and contribute to standards for experimentation, data quality, and reproducibility

What We Are Looking For

3–8 years of experience in data science, applied statistics, machine learning, or a closely related quantitative field, with demonstrated experience delivering analyses or models used in production decisions

Advanced Python and SQL skills, including pandas, NumPy, data manipulation, query optimization, and experience working with large or distributed datasets

Strong foundation in statistics and experimental design, including hypothesis testing, confidence intervals, power analysis, causal inference, and A/B testing

Hands-on experience developing and evaluating machine learning models with scikit-learn, XGBoost, PyTorch, TensorFlow, or equivalent tools

Experience with modern data platforms and workflows, including a cloud warehouse or lakehouse, dbt, Spark, Airflow, or comparable technologies

Bachelor’s or master’s degree in statistics, mathematics, computer science, economics, engineering, or another quantitative discipline

Bonus: Experience with LLM evaluation, recommender systems, time-series forecasting, geospatial data, causal ML, or MLOps; familiarity with Docker, Kubernetes, and cloud services such as AWS, GCP, or Azure