Staff Data Scientist (AI​/ML

Job Title: Staff Software Engineer, AI

Locations: Houston, TX or Boston, MA (Hybrid 2 Days in Office)

Reports to: VP, AI Engineering, AI Platform

As a Staff Data Scientist, you will be a key technical leader responsible for shaping the strategy, development, and deployment of scalable, reliable, and innovative AI/GenAI and machine learning solutions. You will lead high-priority initiatives, set technical direction for data science programs, and ensure alignment with organizational goals. This role demands a high degree of expertise in machine learning, statistical modeling, and applied AI, along with strategic thinking and the ability to collaborate effectively across diverse teams while mentoring and elevating others to meet a very high technical bar.

This is one of the critical roles in the project, where you will be an expert in product development, a good team player, and will lead and mentor your team members. We believe in using the best tools for the task at hand so the ability and desire to learn new programming languages and technologies is necessary. All of this adds up to an exciting, challenging, and always interesting place to work, where complex problems are found and solved every day.

This role determines the root cause for the most complex software issues and develops practical, efficient, and permanent technical solutions.

Educational Background. Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field;
Ph.D. is preferred but not required.

Experience. 10+ years of professional experience in data science, machine learning, or AI, including 5+ years working on AI/ML or GenAI solutions. Proven track record of developing, deploying, and scaling production-grade machine learning models and data-driven solutions.

• Deep expertise in Python and frameworks such as Tensor Flow, PyTorch, Scikit-learn, Pandas, and Lang Chain.
• Advanced knowledge of machine learning algorithms, statistical modeling, experimentation methodologies, generative models, and LLMs.
• Proficiency with cloud platforms (e.g., GCP, AWS, Azure) and modern MLOps practices.
• Strong understanding of feature engineering, model evaluation, causal inference, data pipelines, and database systems (SQL/No
  
  SQL).

Leadership Skills. Demonstrated ability to lead complex data science initiatives, influence cross-functional teams, and mentor data scientists at all levels.

Problem‑Solving Skills. Exceptional analytical and problem‑solving skills, with a proven ability to navigate ambiguity and deliver impactful, data‑driven solutions.

Collaboration. Excellent communication and interpersonal skills, with the ability to engage and inspire both technical and non‑technical stakeholders.

• Define and drive the data science and AI/GenAI vision and roadmap, aligning with company objectives and future growth.
• Provide technical leadership for complex, large‑scale machine learning and AI initiatives, ensuring scalability, performance, accuracy, and business impact.
• Act as a thought leader in AI, machine learning, and data science, influencing cross‑functional decisions and long‑term strategies.

• Lead the development of state‑of‑the‑art generative AI solutions, leveraging advanced techniques such as transformer models, diffusion models, predictive modeling, and multi‑modal architectures.
• Drive innovation by exploring and integrating emerging AI technologies, machine learning methodologies, and data science best practices.
• Mentor data scientists and machine learning practitioners, fostering a culture of continuous learning and technical excellence.
• Elevate the team’s capabilities through coaching, training, and providing guidance on modeling approaches, experimentation, statistical rigor, and complex problem‑solving.

• Take full ownership of high‑impact initiatives, from problem formulation and experimental design to model development, deployment, and monitoring in production.
• Ensure the successful delivery of projects with a focus on measurable business…