Back
Tier-1 brand, mid-level generalist ML title, metro location, and broad requirements increase competition.
Core ML, MLOps and CV skills are transferable across industries but fintech product context adds domain preference.
Explicit 3–5 year requirement plus mandatory production ML, Python/SQL, and MLOps skills make filters strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and deploy scalable machine learning models for offline batch, real-time online, and edge computing environments.
Analyze large volumes of complex data to extract actionable insights supporting strategic business initiatives.
Collaborate with engineering, product, and business teams to define challenges and deliver data-driven solutions, while communicating findings to diverse stakeholders.
3 to 5 years of professional experience in a Data Scientist role with proven deployment of machine learning models in production.
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, or related quantitative field.
Advanced proficiency in Python and SQL.
Strong understanding of traditional machine learning algorithms, statistical modeling, linear algebra, and probability theory.
Experienced in building and scaling ML systems across varied environments including real-time and edge computing.
Skilled at synthesizing complex quantitative analyses for both technical and executive audiences.
Capable of working cross-functionally with engineering and product teams to solve ambiguous, high-impact business problems.