About the Role :

We are looking for a Data Scientist who combines strong machine learning fundamentals with a hands-on, exploratory mindset. In this role, you will own the full lifecycle of machine learning solutions - from framing the business problem and exploring data to building models and deploying them into production. You will also stay at the forefront of the industry by experimenting with the latest advances in Generative and Agentic AI.

Responsibilities :

- Design, build, and deploy end-to-end machine learning solutions, from problem definition through to production and monitoring.

- Develop and apply supervised and unsupervised learning models to solve a range of business problems.

- Build and refine forecasting models to support planning and decision-making.

- Apply deep learning techniques where appropriate to improve model performance.

- Perform deep-dive analytics and exploratory analysis to uncover patterns, generate hypotheses, and identify new use cases.

- Prototype and evaluate solutions using Generative AI and Agentic AI frameworks, keeping pace with emerging industry trends.

- Write clean, efficient code in Python and SQL to build data pipelines, features, and models.

- Collaborate with engineering, product, and business stakeholders to define requirements and deliver measurable outcomes.

Requirements :

- Strong knowledge of supervised and unsupervised machine learning algorithms.

- Solid working knowledge of deep learning.

- Proven experience with forecasting techniques.

- Demonstrated experience building and deploying end-to-end machine learning solutions.

- Hands-on proficiency in Python and SQL.

- A keen, curious, and self-driven attitude - comfortable getting hands dirty with deep-dive analytics, thinking outside the box, and exploring datasets to surface relevant use cases.

- Familiarity with Generative AI and Agentic AI frameworks, with at least proof-of-concept (PoC) level experience working with the latest trends in the industry.

Preferred Qualifications :

- Experience with cloud platforms (AWS / Azure / GCP).

- Familiarity with MLOps tooling and practices (e.g., model versioning, CI/CD for ML, monitoring).

- Experience with big data technologies (Spark, Databricks, etc.).

- Bachelor's / Master's / PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.

Key Skills :

- Machine Learning : Supervised Learning, Unsupervised Learning, Deep Learning, Forecasting.

- Programming and Data : Python, SQL, Data Wrangling.

- ML Engineering : End-to-End ML Solution Development, Model Deployment, Production Monitoring.

- Emerging AI : Generative AI, Agentic AI Frameworks, Rapid Prototyping (PoCs).

- Analytics and Mindset : Exploratory Data Analysis, Deep-Dive Analytics, Problem Solving, Curiosity, Out-of-the-Box Thinking.