Senior Data Scientist

Analytics Delivery

Design and implement advanced statistical and machine learning models (supervised, unsupervised, and reinforcement learning).
Oversee feature engineering, model training, validation, and deployment processes.
Ensure best practices in reproducibility, model explainability, fairness, and governance.

Leadership & Mentorship

Lead, mentor, and groom junior data scientists by providing technical guidance, regular feedback, and knowledge sharing.
Foster a culture of experimentation, continuous learning, and innovation within the data science team.

Work closely with business stakeholders, data engineers, and translators to frame business problems, design data-driven solutions, and translate insights into actionable recommendations.
Collaborate with solution architects during pre-sales and project scoping to design feasible data science approaches.

Research, experiment, and implement emerging AI/ML techniques to solve complex problems.
Contribute to organizational IP by documenting solutions, creating reusable frameworks, and publishing best practices.
Promote adoption of MLOps practices for model monitoring, scaling, and automation.

Oversee multiple projects simultaneously, ensuring on-time delivery and quality standards.
Lead model testing, UAT, and go-live processes in collaboration with stakeholders.
Act as a subject matter expert in applying analytics solutions across telecom, banking, and manufacturing domains.

Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or related discipline.
5+ years of data science experience, with at least 2 years in a leadership or mentorship role.
Proven track record of delivering end-to-end machine learning projects in telecom, banking, or manufacturing domains.
Strong portfolio of applied ML projects, including deployment and monitoring in production environments.
At least one intermediate or advanced certification in data science, machine learning, or AI.
Experience in client-facing roles and translating business requirements into analytical solutions.

Proficiency in Python (preferred), R, and SQL; hands-on experience with libraries/frameworks such as scikit-learn, TensorFlow, PyTorch, pandas, NumPy, matplotlib/Plotly.
In-depth expertise in Bayesian statistics, regression analysis (beyond linear), supervised/unsupervised learning, time-series forecasting, and NLP.
Strong experience with data platforms (Snowflake, Spark, Hadoop) and cloud ecosystems (AWS, Azure, GCP).
Proficiency in data visualization and storytelling using Power BI, Tableau, or Python visualization libraries.
Understanding of MLOps practices, including containerization (Docker), CI/CD pipelines, and model monitoring.

Strong ability to manage and mentor a data science team in delivering high-quality outputs.
Skilled in working across diverse teams and managing multiple stakeholder priorities.
Ability to clearly articulate technical concepts to senior leadership and non-technical stakeholders.
Experience in project planning, effort estimation, and risk management.