Responsibilities & Key Deliverables
We are looking for a Senior Data Scientist with a balanced mix of hands-on expertise and team leadership capabilities. The ideal candidate is someone who thrives at the intersection of technical depth and strategic impact. In this dual role, you'll own critical projects end-to-end while mentoring a team of data scientists and analysts to drive enterprise-wide AI initiatives.
Leadership (50%)
• Lead and mentor a small team of junior to mid-level data scientists and analysts.
• Translate high-level business problems into analytical frameworks and guide project execution.
• Review models, code, and outputs to ensure quality and scalability.
• Manage timelines, prioritize tasks, and align with cross-functional stakeholders.
• Collaborate with product, engineering, and business teams to deliver AI-driven solutions.
Individual Contributor (50%)
• Design, build, and deploy machine learning models, statistical frameworks, and data pipelines.
• Perform deep data exploration and generate actionable insights.
• Work on a range of problems including predictive modeling, segmentation, recommendation systems,
NLP, and computer vision depending on project needs.
• Conduct robust feature engineering, model evaluation, and tuning.
• Communicate findings clearly to technical and non-technical stakeholders.
Experience
5–8 years of hands-on experience in data science, machine learning, or analytics.
Qualifications
Required Qualifications:
• Strong programming skills in Python, SQL, and proficiency with libraries like scikit-learn,
TensorFlow, PyTorch, or XGBoost.
• Solid understanding of supervised/unsupervised ML, A/B testing, and statistical modeling.
• Demonstrated experience in leading or mentoring data science teams (formal or informal).
• Excellent communication, problem-solving, and stakeholder engagement skills.
• Experience working with both structured and unstructured data at scale.
Good to Have:
• Exposure to cloud-based AI/ML platforms such as AWS SageMaker, Google Vertex AI, or Azure
ML Studio.
• Experience working with cloud-native data tools (e.g., BigQuery, Redshift, Snowflake).
• Understanding of data architecture and modern database systems (e.g., PostgreSQL, MongoDB,
Cassandra).
• Familiarity with MLOps practices, model monitoring, and CI/CD pipelines for ML.