ml engineering manager

Uber

<div><b>About the Team</b>
<br>
<br>
The Maps Search team powers location search and discovery across Uber's ecosystem - enabling pickup and dropoff location search &amp; recommendations for Rides, Eats and other lines of business. This is a full-stack, high-impact team building cutting-edge ML models for retrieval and ranking, planet-scale distributed systems operating at ultra-low latency, and seamless mobile experiences that define how users discover places and interact with the map. Search is a mission-critical surface that directly impacts conversion, reliability, and consumer growth.
<br>
<br>
<b>About the Role</b>
<br>
<br>
As the Senior Engineering Manager, you will lead the entire Maps Search organization (~20 engineers and managers across San Francisco and Toronto at present). This exciting role sits at the intersection of ML/AI innovation, distributed systems, and user experience. Your leadership will directly influence business growth and reliability of one of Uber's most critical entry points.
<br>
<br>
<b>What You'll Do</b>
<ul><li>Partner deeply with Product, Applied Science, and other teams at Uber to define strategy &amp; drive execution on company-critical initiatives.
</li><li>Set the multi-year technical vision and strategy for Maps search and recommendations across Uber
</li><li>Raise the bar on ML innovation and platform reliability at global scale
</li><li>Build and mentor a strong bench of engineering leaders (EMs &amp; Tech leads).
</li></ul><b>Basic Qualifications</b>
<ul><li>10+ years of software engineering experience, including significant experience building or leading ML/AI or optimization-driven systems
</li><li>5+ years of engineering management experience, including managing managers or tech leads
</li><li>Experience leading multi-disciplinary teams (backend services + ML/AI required; mobile experience a plus)
</li><li>Track record of leading mission-critical, consumer-facing products or platforms in close partnership with cross-functional stakeholders
</li><li>Demonstrate experience in developing and retaining high-performing engineering teams.
</li></ul><b>Preferred Qualifications
</b><ul><li>Experience leading large-scale search, ranking, retrieval, or recommendation systems in production
</li><li>Experience taking search or recommendation products from early prototypes to scaled, consumer-facing platforms
</li><li>Demonstrated ability to influence product and business strategy beyond immediate organizational scope
</li><li>Experience leading multi-geography engineering organizations
</li>Strong track record of hiring, mentoring, and developing senior engineering leaders (EMs, Staff+ engineers)<br>
<li>
</li></ul><br>
<br>
For San Francisco, CA-based roles: The base salary range for this role is USD$267,000 per year - USD$297,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$267,000 per year - USD$297,000 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award &amp; other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.</div>

Sr Engineering Manager, Maps Search

Autodesk

<div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div>Job Requisition ID #</div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><div></div>
26WD95896<div></div><div>
Position Overview</div><div></div><div>
We are looking for an ML Engineering Manager to lead and grow the machine learning team within Autodesk&rsquo;s Growth and Experience Technology (GET) organization. Autodesk builds products used by millions of designers, engineers, and creators around the world. Over the next five years, AI will play a defining role in how our customers discover, purchase, and experience our products. Building and executing a strong applied AI strategy within GET is critical to that ambition.</div><div></div><div>
As an ML Engineering Manager, you will have the autonomy and ownership to shape how machine learning drives measurable business impact across Autodesk&rsquo;s go-to-market ecosystem. You will build and scale a high-performing team, define applied ML roadmaps, and lead the delivery of systems that influence real customer experiences at global scale. This is a player&ndash;coach role in a strategically important and fast-evolving area. You will remain technically engaged while growing a team from the ground up&mdash;establishing standards, shaping culture, and building durable ML capabilities that will expand over time.</div><div></div><div>
If you are excited about building teams, owning outcomes, and helping define how AI accelerates Autodesk&rsquo;s next chapter of growth, this role offers both the scope and the runway to do it. Autodesk follows a hybrid work model. You may work remotely or from an Autodesk office, with periodic travel as business needs require.</div><div></div><div>
Responsibilities</div><div></div><div>
Team Leadership &amp; Growth</div><ul><li>Hire, onboard, and develop a high-performing team of machine learning engineers</li><li>Provide clear direction, regular coaching, and performance feedback to support career growth</li><li>Foster a collaborative, inclusive, and high-performance culture that is ambitious, disciplined, and sustainable</li><li>Build strong hiring pipelines and drive the long-term growth and composition of the team</li></ul><div>
Applied ML Strategy &amp; Impact</div><ul><li>Define and evolve the applied ML roadmap for the team, aligning technical priorities with business outcomes</li><li>Own the end-to-end success of ML initiatives&mdash;from understanding customer and business needs to delivering measurable production impact</li><li>Establish a high technical bar across experimentation, evaluation, and production readiness</li><li>Guide architectural decisions and ensure systems are scalable, maintainable, and reliable</li><li>Make pragmatic trade-offs between innovation speed and system reliability to deliver sustainable impact</li><li>Hold the team accountable for translating ML investments into measurable improvements in customer experience and business performance</li><li>Remain technically engaged&mdash;contributing through design reviews, technical deep dives, and hands-on problem solving when needed</li></ul><div>
Cross-Functional Leadership</div><ul><li>Partner closely with product, design, and engineering leaders to align ML initiatives with go-to-market and growth strategy</li><li>Advocate for experimentation rigor and evidence-based decision-making</li><li>Represent and advocate for the team within the broader GET organization and Autodesk AI ecosystem</li></ul><div></div><div>
Minimum Qualifications</div><div></div><ul><li>Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data Science, Statistics, or a related field (or equivalent practical experience)</li><li>8+ years of experience building and deploying production-grade software or machine learning systems</li><li>2+ years of experience leading or managing engineering teams</li><li>Strong understanding of the end-to-end ML lifecycle and production considerations</li><li>Proven track record of delivering data-driven systems that drive measurable impact</li><li>Solid software engineering fundamentals and system design skills</li><li>Demonstrated ability to lead cross-functional initiatives in product-driven environments</li></ul><div></div><div>
Preferred Qualifications</div><div></div><ul><li>Experience leading applied ML efforts in areas such as personalization, ranking, search, or growth-focused systems</li><li>Experience operating in customer-facing, go-to-market, or product-led environments</li><li>Experience scaling teams and building durable engineering processes</li><li>Familiarity with modern ML tooling and cloud-based production environments</li><li>Strong written and verbal communication skills, with the ability to explain technical trade-offs clearly</li></ul><div></div><div>
#LI-AK1</div><div></div><div>
Learn More</div><div></div><div>
About Autodesk</div><div>
Welcome to Autodesk! Amazing things are created every day with our software &ndash; from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.</div><div></div><div>
We take great pride in our culture here at Autodesk &ndash; it&rsquo;s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world.</div><div></div><div>
When you&rsquo;re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!</div><div></div><div>
Salary transparency</div>
Salary is one part of Autodesk&rsquo;s competitive compensation package. Offers are based on the candidate&rsquo;s experience and geographic location. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.<div></div><div>
Diversity &amp; Belonging<br>
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: <div>https://www.autodesk.com/company/diversity-and-belonging</div></div><div></div><div>
Are you an existing contractor or consultant with Autodesk?</div><div>
Please search for open jobs and apply internally (not on this external site).</div></div>

Engineering Manager (ML)

Eaton

<div><div><div><div><h2 class="jobSectionHeader"><b>What you&rsquo;ll do:</b></h2>
</div><div><div>
Job Responsibilities<br>
The Manager will be responsible for:<br>
<ul><li>Lead and manage a team of Engineers to deploy and monitor machine learning models in production.</li><li>Working with data engineers for designing data engineering pipelines and performs robust ETL processes to ensure reliable, high‑quality data for analytics and ML workloads.</li><li>Collaborate with cross-functional teams, including data science, engineering, and operations, to understand business requirements and translate them into scalable ML solutions.</li><li>Architect and implement end-to-end machine learning pipelines for model training, testing, deployment, and monitoring.</li><li>Establish best practices and standards for model versioning, deployment, and monitoring to ensure reliability, scalability, and performance.</li><li>Implement automated processes for model training, hyperparameter tuning, and model evaluation using tools such as Weight and Biases, MLflow, Kubeflow, or similar.</li><li>Design and implement infrastructure for scalable and efficient model serving and inference, leveraging technologies such as Kubernetes, Docker, and serverless computing.</li><li>Develop and maintain monitoring and alerting systems to detect model drift, performance degradation, and other issues in production.</li><li>Provide technical leadership and mentorship to team members, fostering their professional growth and development.</li><li>Stay current with emerging technologies and industry trends in machine learning engineering, and evaluate their potential impact on our processes and infrastructure.</li><li>Collaborate with product management to define requirements and priorities for machine learning model deployments and validation, ensuring alignment with business goals and objectives.</li><li>Implement monitoring and logging solutions to track model performance metrics, resource utilization, and system health, enabling proactive issue detection and resolution.</li><li>Lead efforts to optimize resource utilization and cost-effectiveness of machine learning infrastructure, including compute resources, storage, and data transfer.</li><li>Stay abreast of advancements in machine learning technologies, evaluating their applicability and potential impact on our AI Operations strategy and roadmap.</li><li>Foster a culture of innovation, collaboration, and continuous improvement within the AI Operations team, encouraging experimentation and learning from failures.</li></ul></div></div></div><div><div><h2 class="jobSectionHeader"><b>
Qualifications:</b></h2>
</div><div><ul><li>B.tech / M Tech in Computer Science, Electronics or related fields</li><li>
8 Years +</li></ul></div></div><div><div><h2 class="jobSectionHeader"><b>
Skills:</b></h2></div><p></p><div><br>
<ul><li>
Machine Learning, Software Development</li><li>
Research and development, Technology strategy, Global Project Management, Team Management, Mentoring, Risk Management.</li><li>
Desired Skills :<br>
<ul><li>Masters or Bachelor's degree in Computer Science, Engineering, or related field</li><li>8+ years of experience in software engineering, data engineering, or related roles, with at least 2 years in a managerial or leadership role.</li><li>Experience in Designs and maintains scalable data engineering pipelines and performs robust ETL processes to ensure reliable, high‑quality data for analytics and ML workloads</li><li>Previous experience in a leadership or management role, with a track record of successfully leading technical teams and delivering high-impact projects.</li><li>Experience with version control systems (e.g., Git) and collaboration tools (e.g., GitHub, GitLab) for managing code repositories and facilitating team collaboration.</li><li>Familiarity with infrastructure as code (IaC) tools such as Terraform or CloudFormation for provisioning and managing cloud resources.</li><li>Knowledge of software development methodologies (e.g., Agile, DevOps) and best practices for building scalable and reliable software systems.</li><li>Ability to effectively communicate technical concepts and solutions to non-technical stakeholders, including executives, product managers, and business users.</li><li>Strong proficiency in Python, JAVA and related IDEs</li><li>Awareness of machine learning concepts, algorithms, and frameworks (e.g. TensorFlow, PyTorch, sci-kit-learn).</li><li>Experience with cloud platforms and services (e.g., Azure, AWS, GCP) for building and deploying machine learning applications.</li><li>Proficiency in containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes).</li><li>Hands-on experience with MLOps tools and platforms such as Weight and Biase, MLflow, Kubeflow, TFX, or similar.</li><li>Experience in DevOps and DevSecOps tools and practices</li><li>Strong problem-solving skills and ability to troubleshoot complex issues in production environments.</li><li>Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.</li></ul></li></ul></div></div></div></div>

Engineering Manager - Machine Learning

McCain Foods

<p></p><div><p><b>Position Title: </b>Sr AI Eng Manager, Data AI Analytics</p><p><b>
Position Type:</b> Regular - Full-Time
</p><p><b>Requisition ID:</b> 41091</p><p></p><p><br>
Our Global Technology team&rsquo;s goal is to leverage technology and data to drive profitable growth, focus on enhancing customer experience and to further our purpose of '<b><i>Celebrating real connections through delicious, planet-friendly food</i></b>'. McCain has embarked on an ambitious digital transformation across our business from Agriculture to Manufacturing and commercial capabilities to enhance our Customer obsession. As part of this transformation, we are making significant investments into our digital platforms, technology transformations and in building a data driven culture. We are building digital products for our customers, suppliers/growers, and McCain team members to enable digital processes and data-driven automation. Through our investments, we will transform McCain into a company that empowers our teams with easy-to-use systems which will help them collaborate better, be productive and make data driven decisions. Will you be part of this exciting journey?</p><p></p><p><br>
Our Global Technology team&rsquo;s goal is to inspire growth through innovative use of technology and data. McCain has</p><p>
embarked on an ambitious digital transformation across our business from Agriculture, Manufacturing, Supply Chain to how we serve our customer and consumers.</p><p><b>
Overview</b></p><p>
McCain seeks a visionary, hands-on Senior Engineering Manager to lead our Infrastructure AI, Automation &amp; Tools team. This pivotal role blends the latest in Agentic AI, cloud-native platforms, automation, and advanced developer tooling. You will be responsible for establishing the intelligent automation backbone for McCain&rsquo;s digital transformation, focusing on resilient global infrastructure and seamless integration with business processes.</p><p></p><p><b><br>
The Sr Engineering Manager - Infrastructure AI, Automation &amp; Tools, is a critical hands-on technical Enginnering leadership role in driving McCain&rsquo;s 2030 strategy, enabling digital transformation and modernization of Infrastructure Engineering with AI First, Automation first mindset across the enterprise.</b> This Engineering leader will transform global IT infrastructure services with GenAI, and Agentic AI. This role require deep hands-on skills and not a people manager role, however they will have a team of bright engineers from IITs/BITS woking with them.</p><p><b>
This role will embed AI/ML/GenAI/Agentic AI first automation approach in Infrastructure transformation tracks, Cloud, DevOps, Site Reliability Engineering (SRE), observability, AIOps, and Service Now areas to bring autonomous Enginenering into the DNA of Infrastructure Engineering and Operations</b>. A strong AI and Automation first, background is essential.</p><p></p><p><b><br>
Key Responsibilities</b></p><ul><li><b>
Strategic Technical Leadership:</b> Provide technical direction and leadership to a cross-functional engineering team specializing in Infrastructure AI, cloud solutions, automated operations, and developer tooling. Cultivate a high-performance, innovation-driven culture.</li><li><b>
Agentic AI Enablement:</b> Architect, deploy, and scale Agentic AI systems that perform adaptive, autonomous operations&mdash;including multi-agent coordination, intelligent decision-making, and proactive remediation&mdash;across cloud and hybrid environments.</li><li><b>
Developer Tools Innovation:</b> Lead the creation and integration of intelligent developer tools (e.g., code assistants, automated testing, observability integrations) to increase engineering productivity and software reliability.</li><li><b>
ITSM &amp; DevOps Integration:</b> Engineer seamless, automated integrations between ServiceNow (ITSM/ITOM) and Jira (Agile/DevOps), enabling bi-directional workflow automation for incident, change, and problem management. Ensure traceability, auditability, and governance across digital operations.</li><li><b>
AI Orchestration &amp; Event Automation:</b> Implement and champion the adoption of AI orchestration tools (such as Airflow, Kubeflow, Azure ML Pipelines) to automate, monitor, and manage distributed agent workflows, data pipelines, and ML operations. Integrate orchestration platforms with observability and incident management for true event-driven automation.</li><li><b>
GenAI &amp; Agentic AI&ndash;Driven Autonomous Operations: </b>Deploy autonomous IT operations powered by GenAI and Agentic AI across all infrastructure domains. Champion and partner with vendors and internal teams to accelerate automation of IT operations using GenAI and operational Agentic agents. Integrate GenAI into ServiceNow ITSM for automated incident, request, and change workflows. Apply AI-driven automation to network operations for predictive performance, anomaly detection, and self-healing.</li><li><b>
Collaboration &amp; Stakeholder Engagement:</b> Partner closely with cybersecurity, data science, enterprise architecture, operations, and business leaders to ensure AI and automation initiatives are business-aligned, secure, and deliver tangible value.</li><li><b>
Program Management Rigor:</b> Apply rigorous project management discipline&mdash;enforcing stage gates, entry/exit criteria, and structured feedback&mdash;to ensure high-quality delivery and continuous improvement across multiple parallel initiatives. Build a culture of automation-first, AI-augmented, and customer-centric service delivery.</li></ul><p>
Establish strategic partnerships with vendors focused on GenAI, Agentic AI, and next-generation automation.</p><p></p><p><b><br>
Key Technical Skills &amp; Experience</b></p><ul><li><b>
Agent-oriented programming, multi-agent system design, and agent orchestration frameworks</b> (BDI architectures, event-driven agent models)</li><li><b>
Reinforcement learning, generative AI models (LLMs),</b> causal/predictive modeling for autonomous infrastructure optimization</li><li><b>
Advanced proficiency with AI orchestration and workflow</b> tooling (Airflow, Argo, Prefect, Azure ML Pipelines, Kubeflow)</li></ul><ul><li><b>
Delivering Agentic AI solutions in production</b> (multi-agent collaboration, automated remediation, autonomous diagnostics)</li><li><b>
Programming Expertise:</b> Advanced proficiency in Python (preferred) or Go, with working knowledge of Java and/or C++ for scalable AI agent and LLM/SLM system development.</li><li><b>
AI/ML Model Development:</b> Deep understanding of machine learning fundamentals, including supervised and unsupervised learning, model training, tuning, and evaluation.</li><li><b>
Expertise in developing, deploying, and fine-tuning LLMs/SLMs</b> using modern frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers).</li><li><b>
Experience with prompt engineering, retrieval-augmented generation (RAG),</b> model quantization, and optimization for resource-constrained deployments.</li><li><b>
Natural Language Processing (NLP):</b>Hands-on experience with tokenization, entity recognition, text classification, semantic search, and conversational AI system design. Familiarity with leading NLP libraries (spaCy, NLTK, Hugging Face).</li><li><b>
Agentic AI &amp; Orchestration:</b>Demonstrated ability to design and build agent-based solutions, including agent workflow planning, multi-agent coordination, and memory management. Proficient in orchestration platforms/frameworks such as LangChain, CrewAI, AutoGen, RooCode, or similar for managing agentic workflows and task automation.</li><li><b>
Experience developing knowledge graphs, ontologies</b>, and reasoning algorithms to support agent autonomy and explainability.</li><li>
Familiarity with chain-of-thought prompting, error recovery, multi-agent collaboration, and emerging protocols (MCP, A2A) for agentic communication.</li><li>
Commitment to staying current with industry trends (via research, courses, developer blogs), and to thorough documentation for reproducibility and knowledge sharing.</li></ul><ul><li>
Experience with Git-based version control, GitHub CI/CD automation, and responsible AI practices.</li><li>
Integrating ITSM (ServiceNow) with DevOps (Jira) and operational event feeds for real-time, closed-loop automation. Expert in ServiceNow-Jira integrations for automated ITSM and DevOps workflows, leveraging platforms such as IntegrationHub, Exalate, custom API/webhook solutions</li><li>
Experience in Azure Devops to Jira Plugins and integrations</li><li>
Deploying and scaling AI orchestration tools for enterprise ML/Ops, agent scheduling, or large data pipeline automation</li></ul><p><i>
McCain Foods is an equal opportunity employer. As a global family-owned company, we strive to be the employer of choice in the diverse communities around the world in which we live and work. We recognize that inclusion drives our creativity, resilience, and success and makes our business stronger. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, sex, age, veteran status, disability, or any other protected characteristic under applicable law.</i></p><p></p><p><i><br>
McCain is an accessible employer. If you require an accommodation throughout the recruitment process (including alternate formats of materials or accessible meeting rooms), please </i><i>let us know</i><i> and we will work with you to find appropriate solutions.</i></p><p></p><p><i><br>
Your privacy is important to us. By submitting personal data or information to us, you agree this will be handled in accordance with McCain&rsquo;s </i><i>Global Privacy Policy</i><i> and </i><i>Global Employee Privacy Policy</i><i>, as applicable. McCain leverages AI in the hiring process, though all final decisions are made by humans. You can understand our approach to AI and how your personal information is being handled </i><i>here</i><i>.</i></p><p></p><p><b><br>
Job Family: </b>Information Technology
<br>
<b>Division: </b>Global Digital Technology<br>
<b>Department: </b>Global AI Engineering India
<br>
<b>Location(s):</b> IN - India : Haryana : Gurgaon</p><p><b>
Company: </b>McCain Foods(India) P Ltd</p></div>

Sr AI Eng Manager, Data AI Analytics

Amazon.com

<div><h2 class="jobSectionHeader"><b>DESCRIPTION</b></h2><p>
RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability &amp; fulfilment, buying experience and post-buying experience (product quality and customer returns).<br>
<br>
As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and visual), supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, product similarity, using GenAI, LLMs, NLP and Computer Vision.<br>
<br>
Key job responsibilities<br>
As an Applied Science Manager, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will Lead scientists on the team and oversee research and development projects at various stages ranging from initial exploration to deployment into production systems. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will create the environment in the team to file for patents and/or publish research work where opportunities arise. You will impact the large product strategy, identifies new business opportunities and provides strategic direction to the team.</p><h2 class="jobSectionHeader"><b>
BASIC QUALIFICATIONS</b></h2><ul><li>Knowledge of ML, NLP, Information Retrieval and Analytics</li><li>Master's degree in Computer Science, Statistics, Electrical Engineering, or Mathematics with specialization in specialization in Machine Learning, statistical modeling, or Deep learning.</li><li>5+ years of working experience in solving machine learning problems and deploying science solutions for large-scale applications</li><li>2+ years of experience leading a team of scientists and engineers Knowledge of programming languages such as C/C++, Python, Java</li><li>Excellent written and verbal communication skills</li></ul><h2 class="jobSectionHeader"><b>
PREFERRED QUALIFICATIONS</b></h2><ul><li>Experience building machine learning models or developing algorithms for business application</li><li>Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers</li></ul><br>
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner.</div>

Manager, Applied Science, RBS Tech

The Home Depot

<div><p><b>Position Purpose:</b></p><p>
We are transforming how merchandising decisions are made through AI-powered solutions and automation. The <b>Data Science Manager &ndash; Micro Space Intelligence</b> plays a critical role in the technical design and delivery of AI-enabled planogram automation capabilities. This role partners closely with Strategy and Process teams to ingest business requirements and translate them into a scalable technical roadmap, overseeing the development of business rules, AI/ML algorithms, and optimization techniques that accurately reflect merchandising intent. As the technical owner of the solution, this leader designs the end-to-end technical strategy to integrate tools and platforms together into a cohesive, scalable process, ensuring the underlying math, automation logic, and learning systems are sound, efficient, and adaptable.</p><p>
As a people and technical leader, the Data Science Manager leads and mentors a team of data scientists and solution engineers, setting coding standards, QA processes, and deployment best practices to ensure solutions are robust, maintainable, and production-ready. Acting as a trusted technical advisor to Strategy and business partners, this role evaluates proposed initiatives for technical feasibility, identifies advanced analytical and AI opportunities, and translates high-level business goals into impactful automated systems. The role also collaborates closely with fellow Data Science Managers to align approaches, manage talent effectively, and deliver clear, executive-level communication that drives alignment across all levels of the organization.</p><p><b><br>
Key Responsibilities:</b></p><ul><li>
40% People Management &amp; Team Leadership - Provide leadership, mentorship and coaching to Data Scientists (individual contributors) and evaluate performance, execution, and contribution; Attract, retain and develop top talent to build an effective and high performing team; Support team in development of business knowledge and technical skills with clear plans and regular assessment of progress; Ensure appropriate processes and job-specific requirements are properly documented; Develop onboarding and training plan for both technical and soft skills that will ensure high level of capabilities within the team; Manage resources effectively to align with strategic initiatives and business needs; Foster collaboration with team members to develop a supportive and engaged team; Support team in the design and development of algorithms and models to use against large datasets to create business insights; Drive team's results and celebrate accomplishments</li><li>
25% Project Management - Prioritize and assign projects by striking a balance between a variety of factors, like funding, return on investment, innovative or challenging work, among others; Manage programs for the team to ensure consistency of execution, balance of resources and quality solutions; Collaborate with program managers and business partners to align on objectives and expectations and ensure project teams deliver against them; Serve as escalation point for any issues or difficulties with specific projects; Provide executive updates to leaders at all levels of the organization on program effectiveness, team accomplishments, summary of findings and recommended solution, and explain internal and external impacts</li><li>
15% Strategy &amp; Business Development - Leverage extensive business knowledge and relationships to seek out new business opportunities to leverage data science and advocate for team's skills; Effectively develop trust and collaboration with internal customers and cross-functional teams; Ensure alignment with IT support model, collaboration, tools and governance to enable data scientists to be most effective in executing their roles; Collaborate with other data science managers across the organization to establish high level strategy and optimize talent management</li><li>
20% Technical Exploration &amp; Development - Seek further knowledge on key developments within data science, technical skill sets, and additional data sources; Participate in the continuous improvement of data science and analytics by developing replicable solutions (for example, codified data products, project documentation, process flowcharts) to ensure solutions are leveraged for future projects; Ensure best practices and vision for data analysis and model productionalization are leveraged</li></ul><p><b><br>
Direct Manager/Direct Reports:</b></p><ul><li>
This position typically reports to Senior Manager or above</li><li>
This position has Direct Reports &amp; manages programs</li></ul><p><b><br>
Travel Requirements:</b></p><ul><li>
Typically requires overnight travel 5% to 20% of the time.</li></ul><p><b><br>
Physical Requirements:</b></p><ul><li>
Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.</li></ul><p><b><br>
Working Conditions:</b></p><ul><li>
Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.</li></ul><p><b><br>
Minimum Qualifications:</b></p><ul><li>
Must be eighteen years of age or older.</li><li>
Must be legally permitted to work in the United States.</li></ul><p><b><br>
Preferred Qualifications:</b></p><ul><li>
Bachelor&rsquo;s or Master&rsquo;s degree in Data Science, Computer Science, Engineering, Applied Mathematics, or a related technical field.</li><li>
7+ years of industry experience in data science, applied machine learning, or software engineering, and 2+ years in a people or technical leadership role.</li><li>
Strong foundation in applied data science with experience building and maintaining production-grade software, coupled with the ability to set technical direction, establish standards, and drive data science and engineering excellence from concept through production at scale.</li><li>
Hands-on experience developing AI-powered tools and platforms that integrate seamlessly with existing enterprise software ecosystems.</li><li>
Proficiency in python and solid understanding of engineering best practices, including APIs, microservices, version control, CI/CD, testing and deployment.</li><li>
Experience designing and operating large-scale data pipelines and distributed systems with AI/ML components, preferably on cloud platforms such as GCP and VertexAI.</li><li>
Familiar with microservices architectures and deploying models and APIs in containerized, cloud-neutral environments.</li><li>
Familiar with or contributed to AI agents or agentic applications in real-world, production settings.</li><li>
Proven ability to translate complex business processes and strategy into scalable, maintainable architectures, including well-orchestrated data and ML pipelines, while clearly communicating technical constraints and trade-offs to non-technical stakeholders.</li><li>
Domain experience in retail, merchandising, e-commerce, or supply chain, or familiarity in space intelligence concepts such as store layout optimization, merchandising analytics, computer vision, and LLM-based solutions.</li></ul><p><b><br>
Minimum Education:</b></p><ul><li>
The knowledge, skills and abilities typically acquired through the completion of a bachelor's degree program or equivalent degree in a field of study related to the job.</li></ul><p><b><br>
Preferred Education:</b></p><ul><li>
No additional education</li></ul><p><b><br>
Minimum Years of Work Experience:</b></p><ul><li>
8</li></ul><p><b><br>
Preferred Years of Work Experience:</b></p><ul><li>
No additional years of experience</li></ul><p><b><br>
Minimum Leadership Experience:</b></p><ul><li>
None</li></ul><p><b><br>
Preferred Leadership Experience:</b></p><ul><li>
None</li></ul><p><b><br>
Certifications:</b></p><ul><li>
None</li></ul><p><b><br>
Competencies:</b></p><ul><li>
Attracts Top Talent: Attracting and selecting the best talent to meet current and future business needs</li><li>
Builds Effective Teams: Building strong-identity teams that apply their diverse skills and perspectives to achieve common goals</li><li>
Builds Networks: Effectively building formal and informal relationship networks inside and outside the organization</li><li>
Business Insight: Applying knowledge of the business and the marketplace to advance the organization's goals</li><li>
Communicates Effectively: Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences</li><li>
Develops Talent: Developing people to meet both their career goals and the organization's goals</li><li>
Directs Work: Provides direction, delegating and removing obstacles to get work done</li><li>
Drives Engagement: Creating an environment where people are motivated to do their best to help the organization achieve its objectives</li><li>
Drives Vision and Purpose: Painting a compelling picture of the vision and strategy that motivates others to action</li><li>
Instills Trust: Gaining the confidence and trust of others through honesty, integrity, and authenticity</li><li>
Interpersonal Savvy: Relating openly and comfortably with diverse groups of people</li><li>
Organizational Savvy: Maneuvering comfortably through complex policy, process, and people-related organizational dynamics</li><li>
Persuades: Using compelling arguments to gain the support and commitment of others</li><li>
Strategic Mindset: Seeing ahead to future possibilities and translating them into breakthrough strategies</li></ul></div><p></p>

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