Data Scientist II

About this Role (Responsibilities)

• Apply a diverse set of expertise including data mining, statistical analysis and machine learning to deliver impactful, objective, and actionable data insights that enable informed business and product decisions
• Collaborate with cross-functional teams, including sales, marketing, and product, to identify business opportunities and develop data-driven solutions that drive growth and engagement.
• Partner with line of business teams and collaborate with product managers, engineers and data scientists to foster data-driven decisions that yield significant impacts
• Able to effectively communicate analysis, insights and recommendations to high-level business partners in verbal, visual and written formats
• Thrive in a dynamic and rapidly evolving environment

Do you have the right ingredients (Requirements)?

• Bachelors in computer science, engineering, math, statistics, economics, or other quantitative discipline; Masters preferred.
• 2+ years of data science experience in an industry environment.
• Have solid statistical and machine learning foundations. Familiar with machine learning concepts (e.g. regression/classification, clustering, offline/online model evaluation).
• Experience with advanced machine learning techniques, including supervised and unsupervised learning, graph algorithms, deep learning (e.g., NLP), recommendation systems, and generative AI.
• Experience with Python and SQL, and ML frameworks (e.g. scikit-learn, Tensorflow, PyTorch)
• Experience with cloud solutions, preferably with AWS tooling (e.g. SageMaker, DynamoDB, Athena, Glue, etc.)
• Experience with model workflow orchestration tool (e.g. Airflow)
• Experience collaborating with engineers, product managers, and other cross-functional teams
• Excellent verbal and written communication skills
• Ability to communicate sophisticated quantitative analysis in a clear, precise, and actionable manner.

Special Sauce (Nice to Haves):

• Experience working on LLM applications, including prompting, RAG, and evaluation.
• Experience in software engineering best practices and tools including object-oriented programming, test-driven development, CI/CD, git, shell scripting, task orchestration.
• Experience shipping machine learning systems in production environments.
• Experience in A/B testing and other experimentation methodologies for effective product launch measurement.