Job Title
Job Description Required Education:• PhD or master's degree in computer science, Data Science, Statistics, Mathematics, Engineering or related field • Bachelor's degree/University degree or equivalent experience in STEM Required Qualifications/Skills/Experience:• 5+ years of industry experience as a data scientist, specializing in ML Modeling, Ranking, Recommendations, or Personalization systems • 5+ years of experience designing and developing scalable and reliable machine learning systems for training, inference, monitoring, and iteration • Strong background of ML/DL/LLM algorithms, model architectures, and training techniques • Proficiency in Python, SQL, Spark, PySpark, TensorFlow or other analytical/model-building programming languages • Proficiency with tools and LLMs • Ability to work independently and collaboratively within a team Preferred Qualifications/Skills/Experience:• Experience in GenAI/LLMs projects • Familiarity with distributed data/computing tools (e.g., Hadoop, Hive, Spark, MySQL) • Background in financial business, like banking, risk management • Should be familiar with capital markets, financial instruments and modeling Overview:• This is a development position for establishing and implementing new or revised applications and programs in the Technology team • Responsible for data extraction and data analysis from structured and unstructured sources • Develop systems to clean results to build predictive and prescriptive models and implement them in a production environment by partnering with technology and business partners • Address complex problems involving financial data with a specific focus on credit risk management • Requires an open and adaptive mindset to learn new and advanced models in LLM and GenAI and bring in innovative solutions to complex business problems Job Duties:• Develop plans and coordinate with teams for all analytical efforts • Manage deliverables in an agile environment and maintain clear communication with all model stakeholders • Present status, issues, and analytical findings to various audience groups like business, technology management, risk review, model governance, etc. • Data modelling and cleaning from internal and external sources • Build predictive and prescriptive models by manipulating and cleaning results • Develop, manage, and deploy analytical solutions using Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs) to production systems using the SDLC process • Implement features through the ML lifecycle (Development, Testing, Training, Production, Monitoring/Evaluation) to ensure scalability and reliability
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