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Medium — popular data science role in a metro with an established brand, tempered by senior requirement.
Medium — ML/deployment skills transfer across industries, but credit-risk and bureau-data expertise increases domain specificity.
High — explicit 5/8 year requirements plus mandatory ML, cloud, Spark and deployment experience.
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Lead development and deployment of analytic solutions in credit, fraud, insurance, and marketing domains, managing lifecycle from design to ongoing monitoring.
Oversee and participate in model development, validation, and governance, applying expertise in machine learning, deep learning, and cloud-based distributed environments (GCP/AWS).
Manage multiple complex projects, lead analytic research teams or interns, and collaborate across functions to deliver strategic, value-added analytic products and services.
Master’s or PhD in statistics, applied mathematics, financial mathematics, computer science, engineering, operations research, or highly quantitative field with 5+ years experience OR Bachelor’s degree with 8+ years relevant experience.
Hands-on experience in machine learning and deep learning; exposure to GenAI, NLP, LLMs preferred but not mandatory.
Experience with cloud-based tools (GCP or AWS), Spark ML, and Pyspark using large datasets in distributed environments.
Work Experience Required: Minimum 5 years with advanced degree or 8 years with bachelor’s in quantitative domain; Hybrid work model requiring minimum two days per week on-site.
Experienced in end-to-end model deployment lifecycle in financial services credit risk management and familiar with credit bureau data and scorecard models.
Able to lead high-impact client projects and innovation labs, managing stakeholder relationships and delivering production-ready analytic solutions under tight deadlines.
Skilled in translating complex technical concepts into clear business recommendations, mentoring junior staff, and driving continuous improvement in analytic processes.