Associate Data Scientist

Collaboration and Communication : Work closely with product owners, product engineers, and stakeholders to effectively communicate findings, insights, and recommendations, fostering a collaborative and knowledge-sharing environment
Problem Solving : Apply data analysis and data science methodologies to address complex business problems, collaborating with cross-functional teams to understand and translate business needs into analytical solutions.
Data Exploration, Cleaning, Analysis and Modelling : Explore and clean diverse datasets, identifying patterns, trends, and outliers to ensure data quality and reliability in subsequent analyses. Devote a significant portion of time to conducting advanced statistical analyses, implementing machine learning algorithms, and developing predictive models to derive actionable insights from large datasets
Algorithm Development and Model Deployment: Design and implement algorithms in production environment, create ML pipelines using CI/CD process, develop API, write unit test cases, take signoff approvals and so on
Software Engineering Standards and Documentation : Maintain engineering code standards, prepare documentation
Innovation, Research and Continuous Learning : Allocate time for exploring innovative approaches, researching new technologies, and contributing to the advancement of data science within the organization

What You May Need to be Successful:
Bachelors in any discipline (Preferably Engineering)
2 to 3 years of experience in Artificial Intelligence, data science or a related field.
Proven track record of leading data science projects from conception to deployment
1+ years of experience in developing and deploying generative AI models (e.g., GANs, VAEs, transformers). Skilled in guiding an AI team and leading a group of Data Scientists, Data Engineers, and Analysts
Strong communication skills, with the ability to communicate with Data Science Team Members and Product Owners
Familiar with MLOps, Azure ML Pipeline
Skilled in programming languages such as Python and familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch).
Good understanding of natural language processing (NLP) and computer vision techniques.
Experience in LLM and understanding of RAG architecture is a must
Familiar with cloud platforms (e.g., AWS, Azure) for deploying AI solutions.
Familiar with big data technologies (e.g., Spark).
Strong problem-solving skills and the ability to think critically.
Ability to work collaboratively in a team environment.
Certifications in data science or AI can be a plus.
A portfolio showcasing previous projects or contributions to open-source projects can be beneficial.
Staying updated with the latest research and trends in data science and AI.
Working hours - Required to work with onsite product owner team in the evenings