Data Scientist

Job Description

We’re looking for a Data Scientist who thrives on turning messy, unstructured data into practical insight that drives business action. You will work directly with product managers, engineers, and executive leadership to build machine learning models, design experiments, and develop analytical frameworks that influence critical decisions. This role is ideal for someone who enjoys owning the full lifecycle of data science initiatives—from framing ambiguous problems to deploying predictive systems in production.

You won’t be supporting dashboards; you’ll be embedded in decision-making, applying real-time modeling techniques to high-stakes problems like fraud detection, customer lifetime value forecasting, dynamic pricing, and behavioral segmentation.

Key Responsibilities

• Build and deploy machine learning models (classification, regression, clustering) into production with measurable business impact.
• Design and analyze A/B and multivariate tests; communicate results clearly to non-technical stakeholders.
• Partner with engineering to optimize data pipelines and model inference performance (batch and real-time).
• Develop data-driven frameworks to influence strategy in areas such as churn prediction, lead scoring, and personalized recommendations.
• Regularly perform exploratory data analysis on large, diverse datasets to uncover hidden patterns and strategic opportunities.
• Present technical findings with compelling storytelling to stakeholders across business units and senior leadership.
• Contribute to a culture of experimentation, rigor, and curiosity in the Data Science team.

Qualifications

Required:

• 3–6 years of hands-on experience applying statistical modeling and machine learning to real-world problems (not just academic or Kaggle-based).
• Proficiency in Python (pandas, scikit-learn, PyTorch or TensorFlow) and SQL (writing complex queries across large, normalized schemas).
• Demonstrated experience taking at least one ML model from ideation to production and monitoring performance post-deployment.
• Deep knowledge of at least one domain area: fraud, retention/churn, pricing, recommendation systems, or marketing attribution.
• Strong grasp of causal inference, experimental design, and bias mitigation techniques.
• Comfort working with ambiguity and translating vague questions into analytical frameworks.
• Experience with cloud environments (e.g., AWS Sagemaker, GCP Vertex AI) and version control tools (e.g., Git, DVC).

Preferred:

• Master’s or PhD in a quantitative field (Statistics, Computer Science, Applied Mathematics, Econometrics).
• Experience working in a fast-paced environment such as a Series B+ startup or product-focused tech company.
• Familiarity with tools for data orchestration and model deployment (Airflow, MLflow, Docker).
• Previous work involving time series forecasting or graph-based modeling.
• Ability to mentor junior data scientists and review code for quality and reproducibility.

Are you interested in this position?

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

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