Data Scientist

SQL, Python, AWS SageMaker

Key Responsibilities

• 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.

Qualifications

• 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:

Communication:

Preferred Qualifications

• 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.