Kindly refrain from applying if you do not ave 9+ years of experience
Responsibilities
Research and develop new methodologies, research directions, prototype solutions, and quantify their improvement with rigorous scientific methods in the ML/AI space.
Own all the phases of an R&D project or process improvement, including conceptualization, design, prototyping, documentation, deployment, and monitoring.
Work closely and collaborate with experienced teams in operations, technology, other data scientists, and internal stakeholders during all phases of a project.
Deliver high quality, academic publication-level, documentation of new methodologies and best practices.
Engage with stakeholders on objectives, scope, execution, data exchange, and outcomes for assigned projects.
Participate in and actively contribute to multiple projects simultaneously.
Qualifications
Essential:
Masters (M.Sc.) or Doctorate (Ph.D.) degree in Data Science, Mathematics, Statistics, Engineering, Computer Science, or related field & experience, with outstanding analytical expertise and strong technical skills. At least 9 years of relevant experience.
Domain expert knowledge in multiple areas of the following: programming, software engineering / UI & UX / multivariate statistics (parametric/non-parametric) / machine learning / deep learning & Agentic AI / trend & time-series analysis / sampling theory.
Critical and innovative thinking coupled with strong analytical skills focused on experimentation and hypothesis testing.
High proficiency in Python programming (without AI assistance) and working with large-scale databases (e.g., SQL, Hadoop, pySpark, etc), statistical packages (Pandas, NumPy, Scikit-Learn), and unit testing. Experience with AzureML, DataBricks, and CI/CD (Docker, Unix, AKS) best practices.
Able to work in virtual environment and comfortable with git (Github) processes, including PRs and code conflict resolution.
Strong communication, presentation and collaboration skills in the English language with a record of written materials and public presentations (e.g., papers, conferences, patents, tutorials, substack, etc).
A continuously learner willing to experiment and adopt new technologies and tools.
