Data Scientist

As a Senior Data Scientist, you will be a pivotal expert in, such as NLP, Generative AI, Computer Vision, forecasting, prognostics, or prediction. You will be responsible for independently conducting data science initiatives that directly contribute to key business outcomes, making high-level decisions on DS methodology and technical direction. This role involves designing and delivering robust, scalable, and production-ready DS solutions, often tackling complex problems where standard methods fall short. You will champion knowledge sharing, drive improvements in DS quality and efficiency, and collaborate with business stakeholders to define and align data-driven strategies. Your influence will extend across multiple teams and product areas, driving cultural change towards data-informed decisions.

• 6+ years of experience in advanced ML and statistical techniques
• 3+ years of experience in Cloud Platforms (GCP Proferred)
• 2+ years of experience in leveraging GenAI, NLP for Problem Solving

• Expertise in implementing Data Science Pipeline using Python and SQL

  #LI-SK2

• Lead & Deliver DS Solutions:
  • Design and deliver production-grade DS solutions, including for complex business problems where standard methods fall short.
  • Lead DS methodology, tool, and platform selection from an opinionated stack.
  • Independently conduct DS initiatives directly driving key business outcomes.
  • Drive team delivery on time and with high quality, partnering with product owners.
• Technical Expertise & Innovation:
  • Implement, rigorously customize, and productionize sophisticated algorithms and statistical models, tailoring them to unique data characteristics and business constraints.
  • Design innovative, scalable DS solutions, evaluating complex trade-offs (e.g., accuracy vs. interpretability vs. fairness).
  • Pioneer the application of appropriate methodologies, leveraging subject matter and data expertise to solve challenging business problems for practical impact.
  • Anticipate future analytical needs and build extensible systems/models.
• Quality & Best Practices:
  • Build robust DS pipelines meeting Service Level Objectives related to model accuracy, prediction freshness, and data drift.
  • Advocate for the adoption of quality tools/practices in DS workflows.
  • Implement sophisticated testing/validation strategies (e.g., bias detection, robustness checks).
  • Produce exemplary code that establishes patterns for DS work (e.g., reusable feature engineering, model factories).
  • Lead reviews of DS work (methodology, code, results) and establish review standards for DS projects.
  • Drive DS quality and rigor across teams.
  • Champion DS standards & best practices.
• Collaboration & Mentorship:
  • Oversee work of GSR 6-7 data scientists on the team, conducting proper model evaluation.
  • Partner with business stakeholders to define data-driven strategy and align DS roadmap.
  • Mentor others across the team in advanced DS topics and best practices.
  • Collaborate with and advise management and diverse stakeholders, translating complex data insights into strategic implications.
• Documentation & Communication:
  • Author documents for comprehensive methodology & architecture of complex DS systems, and for roadmaps & strategy of DS projects.
  • Communicate complex DS concepts clearly in writing.
  • Improve existing DS documentation standards.
  • Communicate business requirements and socialize model results.
• Deployment & Operations (MLOps):
  • Design robust, scalable deployment & retraining strategies.
  • Implement sophisticated release management for ML models.
  • Lead resolution of critical ML system incidents, conduct RCA, and implement permanent solutions.
  • Implement advanced monitoring (e.g., explainability, drift, fairness metrics) for their area.
  • Create proactive alerting systems.
  • Build observability into DS solutions and drive model improvement based on monitoring data.
• Strategic Influence & Learning:
  • Identify strategic opportunities for data-driven value creation.
  • Influence organizational priorities through data insights & expertise.
  • Evaluate emerging technologies/research for potential business applications.
  • Showcase learning, share best practices, and contribute to internal DS knowledge base.
  • Manage stakeholder expectations for specific projects.