Data Scientist

Seeking a highly skilled Data Scientist along with strong Data Engineering expertise to join Advance Analytics and Modeling team. The ideal candidate will have a proven ability to work with diverse datasets, build machine learning models, and deploy scalable solutions.

This role requires proficiency in Python, advanced analytical skills, and experience in developing machine learning models and dashboards in python. The candidate should have experience in deploying machine learning models in production using cloud platforms such as AWS or Azure and workflow orchestration tools like Airflow.

Key Responsibilities :

- Perform statistical analysis to derive actionable insights from data.

- Design and implement predictive analytics solutions to support business decision- making.

- Design, build, and optimize data pipelines using Airflow.

- Ensure data quality, integrity, and availability through robust ETL processes.

- Work on model deployment using Python-based frameworks and containerization (e.g., Docker)

- Create interactive dashboards using Python libraries to visualize data and results.

- Collaborate with cross functional teams to understand business requirements and translate them into analytical solutions.

Required Skills & Expertise :

- Proficiency in Python, advanced Python concepts (object-oriented programming, multi-threading, etc.), SQL and Excel.

- Hands-on experience with data visualization tools (e.g., Tableau, Power BI) is a plus.

Analytics and Machine Learning :

- Strong analytical skills and experience in predictive modeling.

- Hands-on experience with machine learning libraries like scikit-learn, TensorFlow or PyTorch.

- Ability to work with large datasets and extract meaningful insights to generate
output

- Ability to work on ad-hoc requests to support business needs and derive actionable insights

Data Visualization :

- Experience inn creating dashboards using Python libraries like Plotly, Dash, or Streamlit.

Data Engineering :

- Knowledge of Airflow or similar workflow orchestration tools.

- Expertise in ETL processes and pipeline automation.

Model Deployment :

- Familiarity with deploying machine learning models in production using Flask, FastAPI, or similar frameworks.

- Experience with containerization and orchestration tools (Docker, Kubernetes.)

Tools and Platforms :

- Experience with version control tools like Git.

- Familiarity with cloud platforms like AWS, Azure, or GCP is a plus.

Problem-Solving & Communication :

- Strong problem-solving skills, with the ability to translate complex analytical findings into clear and actionable recommendations.

- Excellent communication and presentation skills, with experience delivering insights to senior stakeholders.

Candidate Profile :

- Professional Experience : At least 5 years of experience in Data Science and Data Engineering.