• Own the end-to-end lifecycle: problem framing → data readiness → modeling → deployment → monitoring → iteration • Define and execute roadmap areas like anomaly/event detection, asset/process health, root-cause support, optimization, and closed-loop decision support • Partner with industrial customers and SMEs to translate real process constraints into ML/optimization/decisioning solutions • Drive unsupervised/self-supervised initiatives • Lead and mentor DS talent, set processes, frameworks and quality standards • Build and deploy AI / ML solutions / models in production

• Develop and execute sales and marketing strategies to grow market share in the transportation and trucking sectors. • Lead marketing campaigns, drive brand awareness, and generate high-quality leads across digital and traditional channels. • Establish, nurture, and manage client relationships with fleet owners, logistics partners, and industry stakeholders. • Monitor and analyze industry trends, competitor activity, and market data to maximize opportunities. • Work closely with cross-functional teams for seamless service delivery and consistent customer satisfaction. • Build, manage, and expand a strong pipeline of prospective business clients using outbound and inbound strategies. • Identify new markets and segments for growth, introducing tailored solutions that meet customer needs. • Craft compelling sales presentations, proposals, and marketing materials for diverse audiences. • Track key metrics to evaluate campaign effectiveness, adjusting tactics to optimize ROI. • Represent the company at events and industry trade shows to enhance visibility and strengthen partnerships.

• Review, refine, and enhance AI-generated responses related to organizational design, operating models, change management, and transformation strategy • Evaluate content for technical and conceptual accuracy, real-world applicability, and best-practice alignment • Draft realistic organizational scenarios such as restructuring, governance redesign, team effectiveness, and scaling challenges • Create scenario variations from perspectives including HR leader, operational executive, consultant, and board member • Spot flaws in reasoning, gaps in process logic, or misinterpretations of organizational theory and practice

• Review and refine AI-generated content related to employment law, workplace policies, and HR regulations • Assess the accuracy, clarity, and compliance of AI outputs with relevant EU/UK employment laws and best practices • Create and enhance realistic employment scenarios, covering onboarding, disciplinary actions, contracts, compliance, and dispute resolution • Generate scenario variations from multiple perspectives, such as legal counsel, HR professionals, and business stakeholders • Identify legal reasoning gaps, potential compliance risks, or improvement areas in AI handling of employment law matters • Support the development of practical AI-powered tools for HR documentation and employment compliance