Data Scientist

Job Description

The ideal candidate is a strong researcher with a practical engineering mindset—comfortable developing novel models, testing hypotheses, and deploying solutions that drive measurable business impact. Key Responsibilities

Algorithm & Model Development

• Develop, test, and optimize machine learning models, predictive algorithms, and statistical frameworks for product and marketing applications.
• Conduct advanced data analysis using techniques such as Bayesian modeling, time series forecasting, causal inference, reinforcement learning, anomaly detection, or optimization algorithms.
• Build scalable solutions and partner with engineering teams to deploy models into production.

Cross-Functional Collaboration

• Work with product teams to inform feature development, personalization strategies, experimentation design, and product roadmaps.
• Collaborate with marketing to build customer segmentation models, attribution models, LTV forecasting, targeting strategies, and campaign optimization tools.
• Translate scientific findings into clear business recommendations and partner with stakeholders to ensure adoption.

Data Exploration & Insights

• Lead complex research initiatives—hypothesis formulation, experiment design, statistical validation, and result interpretation.
• Analyze large datasets to uncover patterns, generate insights, and identify opportunities for product or marketing optimization.
• Develop dashboards, metrics, and automated analytical systems to monitor model performance and business outcomes.

Experimentation

• Design and evaluate A/B tests, multi-arm bandit experiments, and other controlled experiments to measure product and marketing impact.
• Apply rigorous statistical methodologies to assess causal relationships and ensure scientific validity.

Documentation & Communication

• Document model assumptions, methodologies, and results in a way that is clear, reproducible, and auditable.
• Present findings and recommendations to leadership and cross-functional teams in a compelling and accessible manner.

Qualifications

Required

• Ph.D. in Mathematics, Statistics, Applied Mathematics, Computer Science, or a related highly quantitative field.
• Strong foundation in mathematical modeling, probability, statistical inference, and advanced analytics.
• Proficiency in Python (Pandas, NumPy, SciPy, scikit-learn, PyTorch/TensorFlow preferred).
• Experience building and deploying machine learning models end-to-end.
• Experience working with large, complex datasets using SQL or similar tools.
• Ability to communicate complex technical concepts to non-technical stakeholders.
• Proven ability to work in cross-functional environments and manage multiple research streams.

Are you interested in this position?

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

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