Data Scientist

The role applies statistics traditional machine learning deep learning generative AI and agentic AI to enterprise use cases. The Data Scientist owns model development evaluation and continuous improvement; partners with engineering teams on deployment and operational controls; and mentors junior data scientists while collaborating with business and client-facing teams.

• Translate business challenges into clearly defined AI/ML use cases measurable success criteria appropriate evaluation methods and feasible technical approaches.
• Data quality and readiness: Assess data completeness accuracy representativeness and potential leakage before developing models.
• Design train validate and optimize machine learning models using structured and unstructured data; conduct exploratory analysis feature engineering statistical testing and error analysis.
• Evaluate adapt and fine-tune pre-trained models including NLP deep learning and large language models (LLMs) for specific enterprise and domain requirements.
• Build and improve GenAI and Agentic AI solutions including retrieval-based workflows tool integration agent orchestration evaluation guardrails and human-in-the-loop review where appropriate.
• Develop reproducible training and evaluation pipelines and work with engineering teams to productionize solutions with appropriate versioning deployment and operational controls.
• Own continuous improvement of deployed models and AI agents: monitor accuracy reliability drift unexpected behavior latency and cost; investigate issues and drive model refinement or retraining.
• Collaborate with data engineering application platform and business teams to deliver secure scalable explainable and maintainable solutions.
• Apply responsible AI practices including transparency model observability governance documentation and appropriate privacy and risk controls.
• Document architectural choices experiments model limitations and reusable approaches; conduct technical reviews and mentor junior data scientists.
• Communicate results trade-offs risks and business impact to technical and non-technical stakeholders; contribute technical expertise to solution design and presales discussions.

Special assignments or tasks assigned to the employee by their supervisor as determined from timeto time in their sole and complete discretion.

• Six or more years of hands-on experience in applied data science machine learning or AI including responsibility for model development and production deployment or support.

• • Proficiency in Python and SQL with practical experience in data processing model development and machine learning libraries such as pandas scikit-learn PyTorch or TensorFlow.
  • Ability to apply statistical methods supervised and unsupervised learning feature engineering experiment design model selection and rigorous evaluation.
  • Hands-on experience with NLP and generative AI techniques including LLMs prompt engineering retrieval-augmented generation and model adaptation or fine-tuning.
  • Experience developing or integrating AI agents and evaluating their quality reliability and behavior in enterprise workflows.
  • Understanding of production machine learning practices including reproducible pipelines model registries versioning monitoring troubleshooting and retraining.
  • Experience working with complex structured and unstructured datasets.
  • Familiarity with cloud services such as AWS Azure or Google Cloud Platform (GCP) application programming interfaces (APIs) and container-based deployment concepts.

  Preferred Skills:

  • Familiarity with production machine learning tools such as MLflow or Kubeflow.
  • Exposure to Snowflake or similar enterprise data platforms.
  • Familiarity with Docker Kubernetes or continuous integration and continuous delivery (CI/CD) practices.
  • Experience with LLM observability and evaluation tools such as LangSmith or Langfuse and with enterprise AI security and governance requirements.
  • Analytical and problem-solving skills including the ability to translate ambiguous requirements into practical measurable solutions.
  • Technical ownership and engineering judgment including the ability to balance model quality explainability delivery effort and operating constraints.
  • Clear communication client and stakeholder engagement cross-functional collaboration and technical mentorship skills.
  • Curiosity and adaptability with a disciplined approach to evaluating and applying emerging AI capabilities.

• Medical Insurance
• Vision Insurance
• Dental Insurance
• Paid Time-Off
• 401(k) Retirement Plan with match
• Educational Reimbursement
• Parental Bonding Time
• Employee Discounts