Affine - Data Scientist - Artificial Intelligence/Machine Learning (4-8 yrs)

Key Responsibilities :

- Complete accountability for delivering 1-2 projects from conception to implementation

- Managing the team of Associates and Senior Associates

- Interviewing client intelligently to learn important figures and gather requirements

- Managing project timing, client expectations and meeting deadlines

- Creating smart & impactful PowerPoint presentations

Design and Development :

- Develop machine learning models and algorithms to solve complex business problems.

- This includes data preprocessing, feature engineering, model training, and optimization.

Model Deployment :

- Deploy models into production, ensuring scalability, reliability, and performance.

- Collaborate with DevOps teams to integrate models into existing systems.

Data Analysis :

- Analyze large datasets to extract actionable insights and identify trends.

- Use statistical methods and machine learning techniques to build predictive models.

Research and Innovation: Stay up-to-date with the latest AI/ML research and technologies. Experiment with new models, frameworks, and tools to improve existing processes and solutions.

Collaboration :

- Work closely with data scientists, engineers, and product teams to understand business requirements and translate them into AI/ML solutions.

Documentation :

- Maintain comprehensive documentation of models, algorithms, and processes to ensure reproducibility and knowledge sharing within the team.

Mentorship :

- Provide guidance and mentorship to junior team members, helping them grow their technical skills and understanding of AI/ML concepts.

Technical Expertise :

- Strong programming skills in Python.

- Proficiency in machine learning libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, etc. Gen AI

- Experience with deep learning, NLP, computer vision, or other AI domains.

- Proficiency in working with databases, SQL.

- Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization (e.g., Docker, Kubernetes).