Data Scientist

Job Description

We’re looking for a Data Scientist who thrives in ambiguity and enjoys building from scratch. You’ll be part of a lean, cross-functional team tackling high-impact problems in behavioral prediction, pricing optimization, and operational decision-making. This role is not just about crunching numbers — we want someone who can translate complex patterns into actionable insights, influence product direction, and deploy models that scale. You’ll work closely with product managers, engineers, and business stakeholders to experiment rapidly, validate assumptions, and own your models from prototype through production.

Key Responsibilities

• Develop and deploy predictive models for churn, conversion, and engagement using real-time and batch data pipelines.
• Design and run controlled experiments (A/B tests) and quasi-experimental methods to guide product decisions.
• Build scalable tools that surface data and models directly into product experiences.
• Dive deep into user behavior data, synthesizing qualitative and quantitative signals to uncover root causes and opportunities.
• Define and track success metrics, and set up monitoring for model drift and degradation.
• Partner with engineering to build clean, version-controlled, reproducible pipelines using modern ML ops practices.
• Communicate findings clearly and persuasively to stakeholders with varying technical backgrounds.

Required Qualifications

• 3–6 years of experience in a data science role with a strong applied focus (ideally in tech, marketplace, or consumer product domains).
• Advanced proficiency in Python and SQL. Experience with ML libraries (e.g., scikit-learn, XGBoost, LightGBM) and distributed data tools (e.g., Spark, BigQuery, or Snowflake).
• Demonstrated experience building and deploying end-to-end machine learning models, including feature engineering, model selection, validation, and monitoring.
• Strong background in experimental design and causal inference. Familiarity with uplift modeling, synthetic controls, or instrumental variables is a plus.
• Excellent communication skills — you can distill a noisy dataset into a clear story and advocate for your point of view with data.
• Experience working with version control, notebooks, and production deployment tools (e.g., MLflow, Airflow, or Vertex AI).
• Comfortable in ambiguous, fast-paced environments — you know how to prioritize and iterate quickly.

Nice to Have

• Experience working with product analytics tools like Amplitude, Mixpanel, or Segment.
• Knowledge of deep learning frameworks (e.g., PyTorch or TensorFlow) or experience with NLP/time-series models.
• Exposure to product development lifecycles and experience collaborating closely with engineers on shipping data-powered features.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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