Back
Metro location and known employer increase competition, moderated by seniority and niche ML requirements.
ML engineering skills are broadly transferable, though FinTech preference slightly narrows industry fit.
Multiple mandatory ML, production engineering, and cloud skills imply strict technical screening.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own end-to-end development and deployment of machine learning systems, including data pipelines, training, and real-time prediction engines.
Collaborate cross-functionally with product managers, data scientists, and engineers to develop, prototype, and implement scalable algorithms for real-time recommendations and marketing efficiency.
Run A/B tests, perform statistical analysis, and continuously improve algorithmic models and data processing pipelines.
Bachelor’s degree or higher from an accredited university, preferably in Computer Science or related field (BS, MS, or PhD).
Experience with machine learning techniques (classification, regression, clustering) and principles including training and validation.
Proficiency in Data Science tools and frameworks such as Python, Scikit, TensorFlow, Keras, Pandas, Numpy, SQL, and experience writing production-ready code with version control (Git/Github).
Work Experience Required: Preferably experience in FinTech or Credit Card industry; knowledge of Azure cloud infrastructure and familiarity with Generative AI and LLM models required.
Experienced in operating across data science and software engineering boundaries with strong ownership of end-to-end ML system deployment in production environments.
Skilled at scaling algorithms for real-time recommendation systems and capable of designing and interpreting rigorous A/B testing and statistical evaluation.
Comfortable working in cloud-based infrastructure (e.g., Azure), with familiarity or advantage if experienced in Databricks Delta Lake and MLflow tooling.