Data Scientist

We’re seeking a skilled Data Scientist with expertise in SQL, Python, AWS Sagemaker, Machine Learning (Supervised and Unsupervised Learning techniques) 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.

Key Responsibilities

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

* 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+ years in data science, with a focus on solving traditional ML use cases for both structured and Unstructured Data

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).

* Experience 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.

* Commercial : Analyze customer segmentation, lifetime value (CLV), and campaign performance to identify high-impact NBA opportunities.

* Experience with big data tools (Spark, Redshift) or ML Ops practices.

* Knowledge of NLP, reinforcement learning, or real-time recommendation systems.