Staff Data Scientist (AI/ML)

Staff Data Scientist (AI/ML)

At Conga, we've built a community where our colleagues can thrive. Here you'll find opportunities to innovate and support growth through individual and team development, all within an environment where every voice is heard.

Conga lines up commercial operations so companies run as connected, smarter businesses. By unifying the people and processes that drive commerce, Conga aligns pricing, quoting, contracting, rebates, and communications so teams stay in sync and buyers keep moving forward. The result is trusted decisions, consistent buyer experiences, and profitable growth. More than 10,000 customers worldwide, including over 50% of the Fortune 100®, trust Conga when commercial complexity is high and global impact is on the line.

Job Title: Staff Data Scientist

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 TensorFlow, PyTorch, Scikit-learn, Pandas, and LangChain.

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/NoSQL).

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

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

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 outcomes, quality, timelines, and alignment with organizational goals.

Collaborate with cross-functional teams, including product managers, software engineers, analytics teams, and engineering leadership, to deliver cohesive and impactful solutions.

Act as a trusted advisor to stakeholders, clearly articulating model performance, technical tradeoffs, insights, and business impact.

Champion best practices for machine learning development, experimentation, model governance, and MLOps, ensuring robust and reliable solutions.

Monitor and improve the performance, quality, and reliability of deployed models, conducting root cause analyses and driving preventive measures for long-term success.

Advocate for and lead the adoption of new tools, frameworks, and methodologies to enhance team productivity and analytical capabilities.

Stay at the forefront of AI/GenAI and machine learning research, driving thought leadership and contributing to the AI community through publications or speaking engagements.

Experience with multi-modal models, reinforcement learning, causal inference, experimentation platforms, and responsible AI principles.

Advanced knowledge of GCP technologies such as VertexAI, BigQuery, DataFlow, and large-scale data processing platforms.

Contributions to the AI/ML or data science community through publications, open-source projects, or speaking engagements.

Familiarity with agile methodologies and working in a cross-functional product development environment.