• Utilize statistical and machine learning techniques, high performance data architectures and technologies to build algorithms and perform data analytics. • Utilize methods in the areas of Artificial Intelligence (AL), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP) to design and implement data solutions. • Document best practices and solution frameworks. • Collaborate with team members to develop analytical techniques. • Utilize programming skills in SAS, SQL, SPSS and R or Python to create and design new reports independently. • Conduct in-depth data analysis to develop and test hypotheses. • Provide guidance to other team members on building basic models. • Communicate and present analytical findings to other investigators. • Identify innovative technologies available for analytical areas. • Interpret research protocols and run statistical analysis. • Develop and refine machine learning models to support research and data-driven insights. • Generate charts, graphs and other visualizations to clearly communicate analytical findings. • Assist with scientific publications by preparing data outputs, figures, and supporting documentation.

• Define and evolve Snowflake platform architecture, standards, and best practices. • Translate business goals into a pragmatic technical roadmap and delivery plan. • Lead and mentor data engineers; establish quality bars, review code, and guide execution. • Manage delivery using Agile rituals; align priorities across data, analytics/BI, and application teams. • Design, build, and optimize ELT pipelines on Snowflake with an ELT-first approach (dbt preferred). • Implement modern data ingestion using tools such as Fivetran, ADF, Glue, or Matillion. • Set up orchestration and CI/CD for data using Airflow or Dagster and Git-based pipelines. • Ensure data quality, observability, monitoring, alerting, documentation, and runbooks. • Apply performance tuning and cost optimization in Snowflake, including query profiling and warehouse sizing. • Implement security and governance in Snowflake (RBAC, masking, row access policies, auditing, data sharing). • Facilitate discovery with stakeholders, clarify requirements, and communicate trade-offs and recommendations.

• Own the identification, evaluation, and prioritization of adjacent market opportunities surfaced by Sales, Client Success, Product, and other internal teams • Lead comprehensive TAM (Total Addressable Market) analysis for each potential market, including sizing, segmentation, competitive landscape, and entry requirements • Define what features, capabilities, partnerships, and investments are needed to successfully enter and win in new markets • Build and present market expansion business cases to executive leadership and the board • Continuously monitor market trends, customer needs, and competitive dynamics to surface new growth vectors • Serve as a key voice in setting the strategic direction of the product and solution portfolio • Translate market insights, customer feedback, and competitive intelligence into clear product positioning and roadmap input • Partner with Product Management to ensure the roadmap reflects both current customer needs and future market opportunities • Define and maintain clear Ideal Customer Profiles (ICPs) and buyer personas across existing and target markets • Act as the primary liaison between Sales, Client Success, Product, and Marketing to ensure alignment on market direction and priorities • Establish a systematic feedback loop to capture and act on insights from the field • Present market findings, competitive intelligence, and strategic recommendations to senior leadership on a regular cadence

• Design and build AI-driven solutions to understand consumers and improve market performance. • Partner with engineering, product, and business leaders on predictive models and data products. • Work across the full lifecycle of AI and data science solutions—identifying opportunities, building models, and iterating based on performance. • Operate in a modern data environment with tools like AWS, Databricks, and Python. • Evaluate and apply emerging AI technologies for productivity and new capabilities. • Develop scalable data pipelines and feature engineering workflows using Python, SQL, and cloud platforms. • Own the model lifecycle—training, evaluating, monitoring, and iterating models. • Contribute to ML operations workflows, including infrastructure configuration and API development. • Communicate insights and model results clearly through dashboards and visualizations.