Data Scientist

We're seeking a skilled Data Scientist with expertise in SQL, Python, AWS SageMaker, and Commercial Analytics to contribute to Team. You'll design predictive models, uncover actionable insights, and deploy scalable solutions to recommend optimal customer interactions. This role is ideal for a problem-solver passionate about turning data into strategic value.

• Model Development: Build, validate, and deploy machine learning models (e.g., recommendation engines, propensity models) using Python and AWS SageMaker to drive next-best-action decisions.
• Data Pipeline Design: Develop efficient SQL queries and ETL pipelines to process large-scale commercial datasets (e.g., customer behavior, transactional data).
• Commercial Analytics: Analyze customer segmentation, lifetime value (CLV), and campaign performance to identify high-impact NBA opportunities.
• Cross-functional Collaboration: Partner with marketing, sales, and product teams to align models with business objectives and operational workflows.
• Cloud Integration: Optimize model deployment on AWS, ensuring scalability, monitoring, and performance tuning.
• Insight Communication: Translate technical outcomes into actionable recommendations for non-technical stakeholders through visualizations and presentations.
• Continuous Improvement: Stay updated on advancements in AI/ML, cloud technologies, and commercial analytics trends.

• Education: Bachelor's/Master's in Data Science, Computer Science, Statistics, or a related field.
• Experience: 3-4 years in data science, with a focus on commercial/customer analytics (e.g., pharma, retail, healthcare, e-commerce, or B2B sectors).

Technical Skills:

• Proficiency in SQL (complex queries, optimization) and Python (Pandas, NumPy, Scikit-learn).
• Hands-on experience with AWS SageMaker (model training, deployment) and cloud services (S3, Lambda, EC2).
• Familiarity with ML frameworks (XGBoost, TensorFlow/PyTorch) and A/B testing methodologies.

Analytical Mindset: Strong problem-solving skills with the ability to derive insights from ambiguous data.

Communication: Ability to articulate technical concepts to business stakeholders.

• AWS Certified Machine Learning Specialty or similar certifications.
• Experience with big data tools (Spark, Redshift) or ML Ops practices.
• Knowledge of NLP, reinforcement learning, or real-time recommendation systems.
• Exposure to BI tools (Tableau, Power BI) for dashboarding.