Senior Data Scientist
Summary
Senior Data Scientist at Dimensionless Technologies builds predictive ML models for customer interactions, analyzes commercial analytics like CLV and campaign performance, and deploys solutions on AWS SageMaker. Core technologies: Python, SQL, AWS SageMaker, XGBoost, TensorFlow/PyTorch, Spark, Redshift, Tableau, Power BI.
Compensation: No equity
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.