Information Technology & Software
25 Aug
Director - Data Science & Machine Learning
Job Description:We are looking for an exceptional Lead Data Scientist to build and lead a world-class Data Science team that drives meaningful business outcomes at scale. This is not a pure people-management role. This is a high-ownership, player-coach role for someone who can think strategically, execute aggressively, and lead from the front. You will be expected to identify the highest-leverage opportunities across the business, shape the ML roadmap, drive rapid experimentation, and ship production-grade ML systems that materially move core metrics. You should have a strong track record of building and deploying machine learning systems in production, leading high-performing teams, and solving ambiguous, high-impact business problems. You should be comfortable switching between leadership, architecture, hands-on debugging, stakeholder alignment, and execution review sometimes on the same day .If youre excited by messy real-world problems, high scale, fast decisions, and building with intensity, this role is for you.
We’re looking for someone who can:
Set the bar for what great looks like in applied Data Science and ML execution across the organization.
Define and drive the Data Science roadmap by identifying the highest-impact problems worth solving, not just the loudest asks.
Lead, hire, and grow a high-caliber team of Data Scientists / ML Engineers with a culture of ownership, urgency, and technical excellence.
Own end-to-end delivery of ML initiatives — from business framing and experimentation to deployment, monitoring, and continuous improvement.
Act as a player-coach: unblock teams, review modeling approaches, challenge assumptions, and go hands-on when the situation demands it.
Partner deeply with Product, Engineering, and Business leaders to align on priorities, define success metrics, and drive adoption of solutions.
Build production-grade, scalable, and reusable ML systems with strong engineering rigor and long-term maintainability.
Drive a high-velocity experimentation culture with clear hypotheses, measurable outcomes, and ruthless focus on business impact.
Establish best practices across the ML lifecycle — feature engineering, validation, deployment, monitoring, retraining, governance, and documentation.
Push the team beyond dashboards and incremental models into decision automation, optimization, and durable intelligence systems.
Communicate clearly and influence decisively, especially in ambiguous or high-stakes cross-functional situations.
Raise the organization’s ML maturity by improving trust, adoption, and understanding of Data Science across teams.
Specific Qualifications:
9+ years of experience in Data Science / Machine Learning, with 2+ years of leading teams or managing high-performing DS/ML groups.
Proven track record of shipping ML systems to production that delivered measurable business impact at scale.
Strong experience in applied machine learning, statistical modeling, and optimization, especially in fast-moving business environments.
Deep hands-on expertise with ML frameworks and libraries such as PyTorch, TensorFlow/Keras, Scikit-learn.
Strong understanding of core ML methods including:
Regression
Classification
Tree-based / Gradient Boosting models
Time-Series Forecasting
Ranking / Recommendation systems
NLP
Optimization and decision systems
Strong proficiency in Python, SQL, Pandas, NumPy, and data analysis / visualization libraries such as Matplotlib, Plotly, etc.
Experience building robust ML workflows including:
Feature engineering pipelines
Offline / online evaluation
Experiment design and A/B testing
Model deployment and serving
Monitoring and retraining strategies
Strong understanding of MLOps and production engineering, including:
Dockerization
REST APIs
CI/CD for ML workflows
Version control and reproducibility
Scalable inference and model lifecycle management
Experience with optimization frameworks (e.g. Google OR-Tools) is a strong plus.
Strong intuition for balancing speed vs rigor, and the judgment to know when each matters.Excellent communication and stakeholder management skills, with the ability to challenge, influence, and align senior cross-functional leaders.
Good-to-have:
Experience in Time-Series Forecasting, Market Mix Modeling (MMM), Causal Inference, or Experimentation Platforms.
Experience in personalization, recommendation systems, pricing, logistics, supply chain, or operational optimization.
Experience building ML platforms, reusable modeling frameworks, or internal DS productivity tooling.
Prior experience in consumer internet, e-commerce, food-tech, logistics, mobility, or other high-scale operational businesses.
Strong bias toward building systems that create compounding leverage, not one-off analyses.Passion for raising the technical bar and building teams that attract top-tier talent