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Mid-level ML role in a metro with a well-known fintech brand increases applicant competition.
Core ML skills are transferable, though fintech domain knowledge is beneficial.
Requires explicit years plus mandatory ML, PySpark, deployment and MLOps skills.
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Lead the design, development, and deployment of scalable data science solutions across lending, insurance, investments, and payments domains.
Collaborate with product, engineering, and business teams to identify and deliver measurable business impact using diverse data types including tabular, text, audio, image, and video.
Apply and experiment with state-of-the-art machine learning, deep learning, NLP, computer vision, and Generative AI techniques, while monitoring and refining model performance in production.
Bachelor's or Master's degree in Engineering or equivalent mandatory.
Minimum 2 years of experience in Data Science or Machine Learning.
Strong proficiency in Python, PySpark (for data pipelines and feature engineering), and knowledge of pandas, scikit-learn, Scala, SQL; familiarity with TensorFlow or PyTorch required.
Experience in DevOps/MLOps including Docker container creation and production deployment (e.g., Databricks, Kubernetes).
Experienced in leading end-to-end ML projects with cross-functional collaboration in fintech or similar domains involving lending, payments, insurance, or investments.
Strong technical ownership with hands-on expertise in a wide range of ML techniques including tree-based models, inference, hypothesis testing, and deep learning.
Comfortable working with diverse and complex data types, rapid prototyping, and applying advanced AI techniques to real-world business problems at scale.