Principal Data Science Engineer Data & Analytics [T500 20891]

Job description

About Ferguson: Since 1953, Ferguson has been a source of quality supplies for a variety of industries.

Together We Build Better infrastructure, better homes and better businesses.

We exist to make our customers’ complex projects simple, successful, and sustainable.

We proactively solve problems, adapt and grow to continuously serve our customers, communities and each other.

Ferguson is proud to provide best-in-class products, service and capabilities across the following industries: Commercial/Mechanical, Facilities Supply, Fire and Fabrication, HVAC, Industrial, Residential Trade, Residential Building and Remodel, Waterworks and Residential Digital Commerce.

Ferguson has approximately 36,000 associates across 1,700 locations.

Ferguson is a community of proud associates who operate with the shared purpose of building something meaningful.

You will build a career that you are proud of, at a company you can believe in.

Join Ferguson’s Enterprise Data & Analytics team as a Lead Data Science Engineer, where you’ll take a pivotal role in guiding the design, development, and implementation of various data science initiative .

Using cutting-edge technologies like Azure Machine Learning, AI Services, AI Model Catalog, OpenAI Service, and Databricks, you’ll lead a team of data professionals to create unique business solutions that enable the strategies of the organization.

This position is ideal for an experienced and proactive engineer who is passionate about leading initiatives to enhance data-driven decision-making across the enterprise.

Duties and Responsibilities: Lead the design and deployment of enterprise-grade data science and AI solutions, ensuring scalability, reliability, and performance.

You will leverage tools such as Azure Machine Learning, Databricks, and other AI frameworks to support analytics, machine learning, and automation initiatives.

Architect and lead sophisticated data pipelines and workflows for machine learning (ML) model development, testing, and deployment.

Ensure pipelines are optimized for performance and adhere to MLOps best practices.

Develop, implement, and optimize advanced machine learning models to solve business-critical problems.

Ensure the models are explainable, reliable, and aligned with ethical AI standards Act as a technical advisor for data scientists, AI engineers, analysts, and business collaborators.

Translate complex business requirements into robust AI-driven solutions, ensuring alignment with organizational goals and measurable outcomes.

Identify, troubleshoot, and resolve performance bottlenecks in ML pipelines, data workflows, and AI models.

Lead efforts to optimize model performance and system efficiency.

Ensure all AI and data solutions adhere to governance, security, and regulatory standards.

Implement strong data quality frameworks, monitor alignment to data ethics, and reinforce robust security protocols.

Provide technical mentorship and guidance to junior and mid-level data scientists, AI engineers, and data engineers.

Foster a collaborative, innovative environment that encourages continuous learning and professional development.

Maintain comprehensive documentation of AI models, data science workflows, and technical designs.

Champion knowledge sharing and cross-functional collaboration to improve organizational expertise.

Stay current with advancements in AI, machine learning, and data science technologies.

Proactively propose and implement innovative solutions, such as generative AI, NLP, and predictive analytics, to drive operational excellence and business impact.

Qualifications and Requirements: Bachelor’s degree or equivalent experience in Data Science, Artificial Intelligence, Computer Science, or a related field.

Equivalent practical experience in leading data science and AI projects will also be considered.

Extensive experience with Azure AI/ML tools, including Azure Machine Learning, Databricks, Synapse Analytics, and cognitive services like Azure OpenAI.

Experience with MDM tools like Stibo STEP and Reltio is a plus.

Proven track record of leading technical teams and managing large-scale AI/ML projects, including model deployment, monitoring, and retraining.

Familiarity with Agile methodologies and project management tools.

Strong understanding of MLOps frameworks, CI/CD pipelines, version control systems (e.g., Git), and containerization technologies (e.g., Docker, Kubernetes).

Proficiency in programming languages and tools relevant to data science and AI, including Python, R, TensorFlow, PyTorch, Scikit-learn, and SQL.

Demonstrated expertise in developing and optimizing machine learning models for classification, regression, clustering, and NLP tasks.

Experience with deep learning and generative AI models is a strong plus.

Exceptional problem-solving skills with the ability to analyze complex datasets, uncover insights, and develop innovative, AI-driven solutions.

Strong communication and storytelling skills to effectively convey technical concepts and insights to both technical and non-technical stakeholders.

Commitment to continuous learning and staying updated on the latest advancements in AI, machine learning, and data science technologies, including ethical considerations for AI solutions.

At Ferguson, we care for each other.

We value our well-being just as much as our hard work.

We are committed to a holistic approach towards benefits plans and programs that support the mental, physical and financial well-being of our associates.

Our competitive offering not only includes benefits like health, dental, vision, paid time off, life insurance and a 401(k) with a company match, but our associates also enjoy additional meaningful and inclusive enhancements that are adaptable to their diverse situations and needs, including mental health coverage, gender affirming and family building benefits, paid parental leave, associate discounts, community involvement opportunities and more.

Required Skill Profession

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