• Set and uphold the standard for data-driven decision-making, ensuring our marketing strategies and decisions are backed by data. • Lead the execution of marketing analytics projects, translating raw data into growth opportunities. • Build a team better than yourself - hire, onboard, mentor and inspire a squad of high-performing analysts, fostering a culture where technical mastery intersects curiosity and drive. • Collaborate directly with marketing leaders (Affiliates, Influencers, PPC, SEO, Product Marketing Managers and other) to align analytical roadmaps with our goals. • Take full ownership of delivery lifecycle - from refining complex requirements to prioritizing the backlog, constantly ensuring we're always working on the highest impact bets. • Keep on iterating in terms of team setup and ways of work - integrate AI or other emerging technologies, test decentralised leadership concepts or go full agile - freedom to reinvent the playbook.

Role Description We are currently seeking a Senior Data Scientist - Optimization to join our team in Remote, Texas (US-TX), United States. NTT DATA is seeking a client-facing senior level Data Scientist with deep expertise in Optimization and Operations Research (OR) to lead high-impact consulting engagements. This role blends advanced quantitative modeling, data science leadership, and executive-level client interaction. You will partner closely with clients to solve complex operational and strategic problems using optimization, analytics, and AI-driven decisioning. - Lead and deliver client-facing data science and optimization engagements, ensuring high satisfaction and measurable business outcomes. - Define project objectives, scope, timelines, and success metrics in collaboration with client stakeholders and internal teams. - Establish and maintain strong executive-level client relationships, gaining a deep understanding of business challenges and operational constraints. - Communicate complex mathematical, optimization, and technical concepts clearly and credibly to non-technical and senior audiences. - Prepare and deliver executive-ready updates, insights, and recommendations, including sensitivity analyses and scenario-based findings. - Conduct market research, develop informed perspectives, and communicate thought leadership to clients and internal stakeholders. - Lead the design, formulation, and delivery of operations research and optimization models for large, complex enterprises. - Translate ambiguous business problems into rigorous mathematical formulations and scalable optimization solutions. - Develop and refine models across domains such as: - Resource allocation - Scheduling and workforce optimization - Cost and network optimization - Vehicle routing - Strategic planning (e.g., facility opening/closing, multi-period network modeling) - Apply deep expertise in optimization solvers (e.g., Gurobi preferred, CPLEX or similar) to deliver robust, production-ready solutions. - Scrutinize model outputs, perform sensitivity and trade-off analyses, and clearly articulate implications and recommendations to stakeholders. - Handle data sets of varying complexity, including large-scale, distributed, and streaming data environments. - Assemble, process, and validate large, complex data sets that meet both functional and non-functional requirements. - Implement batch and real-time model scoring to support operational and strategic decision-making. - Apply strong business acumen to analyze data, produce insights, and develop impactful reports and analyses. - Perform ad hoc and exploratory analyses in response to evolving client and business needs. - Collaborate with data engineers, architects, and business stakeholders to resolve data quality, availability, and information flow issues. Qualifications - 7+ years of experience supporting data science projects in a consulting or professional services environment. - 7+ years of experience across one or more of the following areas: Predictive Analytics, Data Design, Statistics, AI / Machine Learning, MLOps. - 2+ years of hands-on experience in Operations Research, Optimization, and Mathematical Modeling. - 3+ years of experience using Python, R, and/or C# to analyze disparate datasets and develop analytical solutions. - Ability to travel up to 25%. Preferred Qualifications & Skills - Master’s degree in Operations Research, Industrial Engineering, Applied Mathematics, Data Science, or a related field preferred. - Demonstrated ability to work independently with minimal guidance while engaging practice leadership as needed. - Strong consulting mindset with exceptional problem decomposition, prioritization, and solutioning skills. - Excellent verbal and written communication skills, with the ability to influence senior and executive stakeholders. - Consistently demonstrates strong teamwork, professionalism, and a results-driven work ethic. - Maintains a flexible, "can-do" attitude and commitment to client and team success. Location & Work Arrangement - United States, with a preference for Atlanta, GA; Louisville, KY; or New Jersey. - Primary work location is remote, with occasional travel to client sites for key workshops, working sessions, and executive presentations. Compensation Where required by law, NTT DATA provides a reasonable range of compensation for specific roles. The starting pay range for this remote role is $144,975.00 - $241,265.00. This range reflects the minimum and maximum target compensation for the position across all US locations. Actual compensation will depend on a number of factors, including the candidate’s actual work location, relevant experience, technical skills, and other qualifications.

• Design, develop, and deploy computer vision and machine learning models to automatically extract map‑observable features from satellite imagery • Leverage and adapt vision foundation models and modern deep learning techniques for geospatial feature extraction at global scale • Build scalable, production‑ready ML pipelines that process large volumes of satellite imagery with high accuracy and efficiency • Integrate ML outputs into HERE’s mapmaking systems, ensuring alignment with cartographic quality, consistency, and accuracy standards • Address real‑world geospatial challenges such as variable imagery resolution, diverse sensors, regional differences, and environmental conditions • Define evaluation metrics, conduct experiments, and drive continuous improvement of model performance and map data quality • Collaborate cross‑functionally with data engineering, mapping, and product teams to translate business needs into ML solutions

• Build growth and opportunity models • Find growth opportunities • Support experimentation and measurement • Build practical data science workflows • Drive action