Sr Data Scientist, Financial LLM

Key Skills

Aartificial intelligence, Apache Hive, Data science techniques, Effective communication skills, Financial Risk, Large Language Models - LLMs, Linux Operating system, Machine learning techniques, Natural Language Processing (NLP), Presentation skills, Project management, Python Programming, Spark-SQL, SQL

Job Description

Qualifications

• NLP knowledge is an advantage, or affinity to textual data
• Deep interest in cutting-edge innovative technologies in NLP, AI, ML, or DL
• Deep technical understanding of underlying DS concepts (not just training models)
• Collaboration with partners across the globe, to deliver complex projects
• Maturity
• Quick learner, adaptable, with the ability to work independently in a fast-paced environment
• Strong verbal and written communication skills. Ability to conduct meetings and make professional presentations, and to explain complex concepts and technical material to non-technical users
• Strong project management and stakeholder management skills

Advantages

• We welcome people who can deliver E2E AI projects (inception to production). We primarily use Python in all stages of development
• Fluent in SQL enough to get the data you need from a warehouse (Vertica, Hive, SparkSQL)
• Comfortable working in a Linux environment
• Experience with building end-to-end reusable pipelines from data acquisition to model output delivery

Responsibilities

• Applies proven methods and hacking skills in working with divergent data types, data scales, and big data - to explore and extrapolate data-driven insights using advanced, predictive statistical modeling and testing applied to data acquired and cleansed from a range of sources
• Uses considerable expertise and independent judgment in collaborating with peers, data engineers, database managers, business analysts, architects and product managers in designing and implementing the research strategy needed to methodically and iteratively structure, extract, cleanse, sample, test, validate, and communicate data-driven insights from complex sources and significant volumes of data for complex and unique business problems.
• Provides guidance and support leadership to business leaders and stakeholders, on how best to harness available data in support of critical business needs and goals
• Leads the full cycle of iterative big data exploration, including hypothesis formulation, algorithm development, data cleansing, testing, insight generation and visualization, and action planning
• Provides business stakeholders with entrepreneurial guidance essential for appropriately interpreting and building on findings, and fully exploiting the insights revealed through the research