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                                                                                                                                                                                                                                                                                                                                                                                                                                                        

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.