Role Description As the Head of Data Science & AI and Implementation at Buyers Edge Platform, you will lead our Data Science & AI transformation and shape the future of our business through innovative AI solutions. This is an exciting opportunity to develop and execute a forward-thinking AI strategy that aligns with our global objectives, driving operational excellence, enhancing data capabilities, and delivering actionable insights. You'll achieve this by collaborating cross-functionally across our platform of brands to integrate AI technologies into our operations, ensuring they deliver measurable value while upholding the highest standards of responsibility and ethics. Your impact: - Lead the team: - Own technical direction, hiring, performance, and career development for a ~20-person org spanning product management, data science, ML engineering, and applied AI. - Oversee the operating model, from project inception to evaluation and maintenance. - Mentor leaders and technical teams while creating scalable operating models. - Serve as a trusted advisor to executive leadership and translate highly technical concepts into actionable business strategy. - Ship into Platform products: - Partner with product and engineering teams across the Buyers Edge Platform organization to determine and deploy data science and AI applications that deliver value, automation, and efficiency. - Direct build-vs-buy calls when it comes to software, tools, data and other assets required for Buyers Edge to succeed in its data science & AI mandates. - Drive enterprise decision support: - Use proprietary and external data sources to build forecasting, anomaly detection, and decision-support capabilities for internal ops, brand presidents, and customers. - Champion KPI frameworks that make model impact tangible for business leaders. - Set the bar for responsible AI: - Establish governance, evaluation, and monitoring standards appropriate for a regulated B2B environment. - Partner with legal, IT and security to ensure compliance and security across the team’s implementations as well as AI tools used by other teams. - Develop policies addressing data privacy, compliance, explainability, model bias mitigation, and responsible AI practices. - Year One Impacts: - Clarify operating model and roadmap for Data Science & AI team, ensuring team buy-in and efficient allocation of resources. - Direct deployment of at least two AI- or ML-powered capabilities into Buyers Edge products with measurable business impact. - Architect and retrofit evaluation and monitor infrastructure for new and existing products. - Develop and execute a company-wide AI strategy that supports Buyers Edge Platform’s long-term growth strategy across our portfolio of brands and solutions. Qualifications - Expert-level proficiency in Python and SQL. - Advanced knowledge of database architecture and schema design. - Hands-on experience with AI/ML frameworks (e.g., TensorFlow, PyTorch) and cloud platforms (AWS, Azure, Google Cloud). - Proven expertise with large language models (LLMs), including prompting and fine-tuning, and predictive AI for time series challenges. - Exceptional ability to align AI strategies with business objectives and drive organizational change. - Strong project management experience, including Agile methodologies. - Outstanding communication skills, with the ability to convey complex AI concepts to non-technical audiences. - Deep understanding of AI ethics, bias mitigation, and explainability techniques. - Experience developing governance frameworks for responsible AI use. - Leverage AI to enhance operational efficiency, customer satisfaction, and revenue growth, contributing to company success. - Collaborate with the executive team to align AI initiatives with organizational goals, using data-driven insights to solve complex challenges. - Champion experimentation and adaptability, driving transformative change in a dynamic environment. - Lead with integrity and emotional intelligence, fostering a collaborative and inclusive culture while mentoring talent for current and future needs. Education - Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Business, or a related field (PhD preferred). - 8+ years of experience in AI, data science, or digital transformation, including 5+ years in a senior leadership role. - Proven success implementing AI strategies in fast-paced, innovative environments (e.g., start-ups or high-growth tech companies). - Extensive knowledge of machine learning, NLP, generative AI, and automation technologies. - Expertise in AI governance, risk management, and regulatory compliance. - Strong business acumen with a track record of communicating AI value to non-technical stakeholders. Benefits - Great benefits from day one. We offer medical, dental, vision, FSA, company-paid life insurance, and more—plus a 401(k) with company match. - Grow with us. Enjoy strong training, development, and competitive pay. - Work-life balance. Our flexible PTO policy lets you take time when you need it—no accrual required. Company Description Buyers Edge Platform stands at the forefront of revolutionizing the foodservice industry through technology, purchasing power and partnerships. We are dedicated to empowering stakeholders across the entire foodservice ecosystem (operators, distributors, manufacturers) with efficiency and unprecedented visibility. With a diverse portfolio of over a dozen brands, our mission is clear: to reduce costs, streamline the foodservice supply chain, and propel the industry from manual to automated. Today, we are one of the largest players in foodservice, with over 200K operator locations across North America and over $50 billion of aggregated spend volume. Our commitment to foodservice excellence is proven in four distinct areas of value: Digital Procurement Network, Fresh Solutions, Supply Chain Management, and Software. Buyers Edge Platform is not just a provider – we are a strategic partner on the journey towards a more efficient, connected, and automated future for the foodservice industry.

• Design, develop and deploy advanced AI/ML models for telecom use cases such as: Network optimization, Traffic prediction and anomaly detection, QoS/QoE modeling and root cause analysis • Apply and adapt state-of-the-art AI techniques, including: Deep Learning (CNNs, RNNs, Transformers), Reinforcement Learning for network optimization, Time-series modeling at scale • Work deeply with real telecom network data (3G/4G/5G), understanding KPIs, counters, and traffic dynamics • Build production-ready solutions, ensuring scalability, performance, and reliability • Collaborate closely with data engineers and telecom experts to integrate AI models into real systems • Act as a technical reference within the team, contributing to best practices and code quality • Explore and apply Generative AI and LLM-based approaches to telecom use cases

• Support o9 model build phase covering EDA, Feature Engg, Experimentation, slicing strategy, parameter tuning and reports review in coordination with RDS • Support resolving model input data defects in coordination with Data Science Leads, BU DP Champions and Data Stewards • Perform experiments supporting value to o9 model building on o9 experiment zone/tenant • Participate in o9 Data Science trainings and upskilling plan • Support Achilles project to harmonize the forecast granularity, driver ingestion, and experimentation • Support during integration testing and user testing for specific business units in scope • Support BU wise Data Quality Improvement track • Build long term sustainable plan for AI/ML Model runs for the specific BU in scope

• Create, develop, and improve machine learning models to enhance business performance and product effectiveness • Collaborate closely with cross-functional teams, including Product Managers, Analysts, and Engineers, to deliver innovative ML-driven features and improvements • Apply expertise in machine learning algorithms, data structures, and optimization techniques to improve model accuracy, scalability, and efficiency • Implement and maintain MLOps best practices for model deployment, monitoring, retraining, and maintenance in production environments • Investigate, troubleshoot, and resolve technical issues related to machine learning models, data pipelines, and infrastructure • Stay up to date with the latest advancements, technologies, and best practices in machine learning, artificial intelligence, and data science