
3 Days Left Data Scientist
3 weeks ago
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: 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.
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.
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