About The Role

As a Senior Data Scientist, you will design and implement sophisticated machine learning models and statistical frameworks to solve complex business challenges. You will go beyond descriptive analytics to build predictive and prescriptive solutions that drive automation and optimization. This role requires a blend of advanced mathematical theory, software engineering principles, and business acumen to move models from experimental stages to production environments.

Model Development: Design, develop, and deploy high-performance machine learning models (supervised, unsupervised, and reinforcement learning) to address business needs such as churn prediction, recommendation engines, or demand forecasting.
Experimental Design: Lead the design and analysis of large-scale experiments (A/B testing, multivariate testing) to validate hypotheses and measure the impact of product changes.
Feature Engineering: Architect and implement robust data pipelines and feature engineering processes to improve model accuracy and scalability.
Algorithm Optimization: Evaluate and refine existing algorithms to improve computational efficiency and predictive power.
Stakeholder Influence: Act as a strategic advisor to leadership, translating complex algorithmic outcomes into business-centric narratives that drive ROI.
Technical Leadership: Mentor junior data scientists and contribute to the team’s internal library of best practices, code standards, and research methodologies.
Collaboration with Engineering: Partner with ML Engineers and DevOps to integrate models into production systems, ensuring reliability and monitoring model drift.
Education: Master’s degree in a highly quantitative field such as Computer Science, Statistics, Physics, Operations Research, Econometrics or Mathematics.
Problem Solving: Demonstrated ability to take an ambiguous business problem and decompose it into a technical data science roadmap.
Communication: Ability to visualize complex data relationships and present findings to both technical and executive audiences.

Desired Experience (Minimum Requirements)

Professional Experience: 5+ years of experience in a Data Science role, with a proven track record of delivering models that impact business outcomes.
Programming Expertise: Expert proficiency in Python (specifically libraries like Pandas, NumPy, Scikit-learn, SciPy, DoWhy, or EconML) or R.
Machine Learning Foundations: Deep understanding of a broad range of ML techniques, including Gradient Boosting (XGBoost/LightGBM), Random Forests, GLMs, and Clustering.
Advanced SQL: Ability to manipulate and extract data from complex, multi-terabyte distributed databases.
Cloud Computing: Experience building and scaling models on AWS (SageMaker), GCP (Vertex AI), or Azure.
Software Best Practices: Experience with version control (Git) and writing clean, modular, and maintainable code.

Preferred Experience

Deep Learning: Experience with Neural Networks and frameworks such as PyTorch, TensorFlow, or Keras.
MLOps: Familiarity with model deployment and monitoring tools like MLflow, Kubeflow, or Docker.
Big Data Ecosystems: Proficiency with distributed processing tools like PySpark or Hive.
NLP/Computer Vision: Specialized experience in Natural Language Processing (LLMs, BERT) or Computer Vision is a significant plus.