Senior Data Scientist

We’re seeking a skilled Data Scientist with 7 + yrs of expertise in Machine Learning algorithms (Supervised and Unsupervised Learning techniques) , SQL, Python, AWS cloud ecosystem. You’ll design predictive models, uncover actionable insights, and deploy scalable solutions to recommend optimal customer interactions.

Key Responsibilities

Model Development: Build, validate, and deploy machine learning models using Python and AWS SageMaker to drive next-best-action decisions. 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: 5-8 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.

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.