Job
Description
Required Skills: 2-5 years of experience in data engineering, analytics and data sciences. Experience in LLM and Gen AI. Proficiency in Python, R, SQL and experience with ML libraries and frameworks like Scikit-learn, NumPy, etc. Familiarity with ML Ops tools/platforms Familiarity with Docker and Kubernetes Proficiency in one or more visualization tools like Tableau etc. Experience engineering information out of massive, complex and, in some cases, unstructured datasets. Ability to apply a strong business sense with technical skills to effectively balance decisions around the complexity and speed of the project delivery. Strong written, verbal, and interpersonal communication skills. Ability to effectively communicate at all levels in the organization. Ability to self-start and self-direct work in an unstructured environment, comfortable dealing with ambiguity. Excellent problem-framing, problem solving and project management skills and ability to change direction quickly. Ability to balance and prioritize multiple projects. Experience working within a Cloud based environment, SaaS. Experience with git and version control workflows. Proficient in performance tuning and debugging Requirements: As a Data Scientist, you will collaborate with a multi-disciplinary team of solution architects and data engineers on a wide range of business problems. You will be an integral part of IT Advanced Analytics group who are a team responsible for building out capabilities across business strategy, analytics, and Cloud. Data Scientist must be able to: Execute on all phases of the Data Science project lifecycle with minimal supervision Interact with business stakeholders to gather requirements and convey project outcomes Job Responsibilities: Be equal member of a cohesive and selfless team. Take complete ownership of your work with the goal of exceeding customer expectations. Work closely with analysts, developers, and data architects to ensure development meets requirements and delivers optimal performance. Work closely with internal WWT business, engineering and technology teams Contribute on all the stages of data science projects: from performing raw data mining to translating complex technical topics into business solutions. Maintains and enhance a set of critical data models supporting our business use cases. Maintains complex data pipeline supporting our team's mission in democratizing data and enabling a data driven organization, partnering with our data engineering teams. Effectively communicate actionable insights at all levels of the organization. Collaborate closely with stakeholders to improve our view of modeling and decision engines. Solve complex problems using advanced mathematical modeling and optimization techniques, including but not limited to, big data pre-processing, problem formulation, features engineering, algorithmic selection and evaluation, hyperparameter tuning for machine learning, and deployment. Build and Maintain models for internal customers and business teams, build knowledge and metrics for the product life cycle. Flexibility to work as a member of a matrix based diverse and geographically distributed project teams. Enhance the subject matter expertise while working with the various business domains.
