Data Scientist

We’re hiring a Data Scientist to turn data into decisions. You’ll design experiments, build predictive models, and partner with product, engineering, and leadership to solve high-

impact business problems — from forecasting demand to personalizing user experiences. This role spans the full lifecycle: problem framing, data exploration, modeling, and

communicating results that drive action.

Key Responsibilities:

Analysis & Modeling (50%):

Design, build, and validate statistical and machine learning models (classification, regression, clustering, forecasting, etc.)

Explore and analyze large, messy datasets to identify patterns and opportunities

Translate ambiguous business questions into well-defined, testable hypotheses

Build and evaluate recommendation systems, churn models, pricing models, or similar (per business need)

Experimentation (20%):

Design and analyze A/B tests and multivariate experiments

Define success metrics and guardrail metrics for product experiments

Ensure statistical rigor (power analysis, significance testing, avoiding p-hacking)

Production & Engineering Collaboration (15%)

Partner with ML/data engineers to deploy models into production pipelines

Monitor deployed models for drift, degradation, and data quality issues

Write production-quality, version-controlled, reproducible code

Communication & Strategy (15%):

Present findings and recommendations to cross-functional stakeholders and leadership

Build dashboards and self-serve tools for non-technical teams

Influence product and business strategy with data-backed insight

Required Qualifications:

Master’s or PhD in Statistics, Computer Science, Mathematics, Economics, or related quantitative field (or equivalent experience)

2–5+ years of professional data science experience

Strong Python skills (pandas, NumPy, scikit-learn, statsmodels) or equivalent R expertise

Solid foundation in statistics and probability (hypothesis testing, regression, experimental design)

Advanced SQL skills; comfort with large-scale data warehouses

Experience designing and analyzing A/B tests

Strong written and verbal communication skills — able to explain technical concepts to non-technical audiences

Preferred / Nice-to-Have

Experience with deep learning frameworks (PyTorch, TensorFlow) for applicable use cases

Experience with cloud-based ML platforms (SageMaker, Vertex AI, Databricks)

Experience deploying models into production (APIs, batch pipelines)

Knowledge of causal inference methods

Domain expertise in [your industry — fintech, healthtech, e-commerce, etc.]

Experience with big data tools (Spark, BigQuery, Snowflake)