Principal 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/Master/PhD in any discipline (Preferably Engineering)
10+ years of experience in Artificial Intelligence, data science or a related field.
Proven track record of leading data science projects from conception to deployment
2+ 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
Experience in collaborating with stakeholders, wider team members, to define project objectives, scope, requirement gathering and deliverables.
Strong communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
Experience in MLOps, Azure ML Pipeline
Proficiency in programming languages such as Python and familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch).
Strong understanding of natural language processing (NLP) and computer vision techniques.
Proficiency in LLM and understanding of RAG architecture is a must
Experience with cloud platforms (e.g., AWS, Azure) for deploying AI solutions.
Experience with big data technologies (e.g., Spark).
It is a HUGE PLUS to have experience in a consulting environment, particularly external (ie. Working for a company for the benefit of another)
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