Description :

We are looking for a Senior Data Scientist who can translate complex business and analytical problems into robust, scalable modeling solutions.

In this role, you will own the end-to-end modeling lifecyclefrom problem framing and data exploration to model evaluation and production handoffwhile working closely with product, engineering, and ML teams.

You will play a key role in driving data-driven decision-making, improving model performance, and ensuring models behave reliably under real-world conditions.

Key Responsibilities :

- Translate well-defined business and analytical problems into structured modeling and experimentation tasks.

- Design, build, train, and evaluate machine learning and deep learning models using appropriate algorithms and techniques.

- Perform detailed error analysis to identify model weaknesses, data quality issues, and edge cases.

- Analyze model outputs, bias, robustness, and sensitivity under real-world and stress-test scenarios.

- Support experimentation frameworks, model comparisons, and evaluation workflows.

- Apply statistical methods to validate model performance and ensure reliable inference.

- Document model assumptions, limitations, expected behavior, and evaluation results clearly.

- Collaborate closely with ML Engineers to support seamless model deployment and production readiness.

- Partner with product and business stakeholders to align modeling outcomes with business goals.

- Continuously improve modeling approaches, feature engineering strategies, and evaluation metrics.

Required Qualifications :

- Strong ability to break down complex analytical problems into tractable and effective modeling approaches.

- Solid foundations in machine learning, deep learning, and time-series concepts.

- Working knowledge of statistics applied to model evaluation, validation, and experimentation.

- Experience working with imperfect, noisy, and real-world datasets.

- Proficiency in Python and common data science libraries (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch).

- Experience designing experiments, selecting metrics, and interpreting results critically.

- Strong communication skills to explain technical findings to non-technical stakeholders.

Preferred Qualifications :

- Experience with model deployment pipelines and production monitoring.

- Familiarity with A/B testing, causal inference, or experimentation platforms.

- Exposure to cloud-based ML environments (AWS, GCP, or Azure).

- Experience in domains such as fintech, consumer tech, SaaS, or data platforms.

- Prior experience mentoring junior data scientists