Lead Data Scientist

We are seeking a seasoned Data Scientist to design, develop, and deploy advanced predictive and intelligent models spanning machine learning, deep learning, natural language processing, and computer vision. This role emphasizes building production-ready solutions with strong statistical foundations, ensuring model reliability, and effectively translating data-driven insights into impactful business decisions.

Key Responsibilities:
- Take full ownership of model development, from experimentation and evaluation to ensuring production readiness.
- Build diverse models including forecasting, optimization, recommendation systems, decision science, and machine learning algorithms.
- Conduct thorough feature engineering, exploratory data analysis, and statistical investigations to uncover meaningful patterns.
- Develop and implement rigorous frameworks for model evaluation, validation, and testing.
- Assess model performance with a focus on fairness and bias mitigation prior to deployment.
- Manage experiment tracking, dataset versioning, and model reproducibility using tools such as MLflow or Weights & Biases.
- Communicate model outcomes, trade-offs, and uncertainties clearly to business stakeholders.
- Collaborate closely with ML engineers and architects to ensure seamless model productionization.
- Lead complex decision intelligence projects as needed.
- Provide mentorship and guidance to junior data scientists and machine learning engineers.

Required Qualifications:
- Minimum of 5 years’ experience in applied data science with a proven track record of shipping production models.
- Deep expertise in classical and statistical machine learning, forecasting, optimization, and deep learning techniques.
- Proficiency in Python and SQL, along with hands-on experience using libraries and frameworks such as scikit-learn, PyTorch or TensorFlow, MLflow, and Weights & Biases.
- Strong foundation in hypothesis testing, confidence intervals, and causal inference methodologies.
- Familiarity with evaluating model fairness and bias, and experience with explainability tools like SHAP and LIME.
- Excellent communication skills, capable of articulating complex technical concepts and model trade-offs to non-technical stakeholders.
- Ability to defend modeling decisions and work collaboratively with engineering and architecture teams.

Preferred Qualifications and Benefits:
Experience with natural language processing, computer vision, graph-based features, or knowledge graphs is considered a plus. The role offers the opportunity to work on cutting-edge projects, lead impactful initiatives, and grow within a collaborative, innovation-driven environment. Mentorship responsibilities provide a chance to shape the next generation of data science talent.