Job Description
The Chief Data Scientist at Eliza will own the strategy, delivery, quality, and growth of all data science and machine learning work across client engagements. Unlike internal-only teams, your data team members work directly on customer projects, so you’ll need to balance technical leadership, consulting discipline, sales/domain alignment, and execution excellence. You will: • Lead and scale the data science / AI practice as a core consulting pillar • Collaborate deeply with sales, solution architecture, and delivery teams • Ensure high-quality, sustainable, scalable AI solutions delivered to clients • Drive innovation, methodology, and domain specialization • Represent Eliza externally (thought leadership, client-facing, industry presence) Key Responsibilities Strategic Leadership & Practice Building • Define the overall vision, strategy, and roadmap for Eliza’s data science / AI practice (aligned with Fusion, Forge, and client engagement tracks). • Identify key verticals, use-case domains, and technical specialization areas to develop deep expertise (e.g. agents, copilots, retrieval-augmented generation, prompt engineering, operational analytics). • Partner with sales and pre-sales teams to help qualify data/AI opportunities, shape solution proposals, and ensure technical credibility. • Drive hiring, training, and career development for data scientists, ML engineers, and analytics consultants — building a bench of billable talent. • Establish metrics and KPIs for utilization, project margin, quality, and client satisfaction for the data practice. Delivery Oversight & Quality Assurance • Oversee delivery of data and AI work on client projects (from discovery, prototyping, to production). • Ensure models and AI systems are robust, maintainable, interpretable, secure, and aligned with governance/compliance. • Define best practices for model validation, monitoring, retraining, ML Ops, error handling, and observability. • Set standards for code, architecture, documentation, data pipelines, and modular AI systems. • Act as “escalation point” for technical risks, ensure client deliverables meet Eliza’s quality bar and commitments. Technical Leadership & Innovation • Keep abreast of advances in LLMs, generative AI, multi-agent systems, embeddings, NLP, and aligned domains. • Drive internal R&D / capability development (e.g. shared libraries, prompt tuning frameworks, agent templates, domain adapters). • Foster knowledge-sharing, internal tooling, and cross-pollination across engagement pods. • Evaluate and select AI/ML frameworks, platforms, infrastructure, and tooling to support scale and repeatability. Client-Facing & Thought Leadership • Serve as a senior advisor for strategic clients on data / AI adoption, architecture, roadmap, and risk mitigation. • Present at conferences, publish whitepapers or blog posts, contribute to Eliza brand in the AI consulting space. • Mentor client teams, build trust with executives, and drive adoption beyond proofs-of-concept. • Governance, Ethics & Risk • Ensure data privacy, security, and fairness practices are built into solutions. • Establish guidelines for responsible AI, bias mitigation, documentation, and audits. • Collaborate with legal, compliance, and security teams to ensure solutions meet enterprise-grade standards. Qualifications & Experience Must-Have: • 10+ years in data science / machine learning / AI, with consulting or client-facing experience • Prior leadership or senior management in a services / consulting environment • Proven track record of delivering data/AI systems (from prototype to production) • Deep technical expertise in Python, data engineering, ML frameworks (e.g. PyTorch, TensorFlow), LLMs, embeddings, NLP, MLOps, etc. • Experience scaling and managing a team of billable data scientists / ML engineers • Strong communication skills, able to bridge between executives, product, engineering, and clients • Business acumen and ability to shape proposals, manage budgets, and ensure margin discipline Preferred / Differentiators: • Master’s or PhD in Machine Learning, AI, Computer Science, Statistics, or related field • Experience specifically with generative AI, agent frameworks, prompt engineering, retrieval augmentation • Domain experience in enterprise workflows, automation, productivity, or process optimization • Consulting mindset: ability to scope client work, manage change, work under ambiguity • Experience contributing thought leadership (public talks, publications) Success Metrics & KPIs • Utilization & Billable Hours: high percentage of data team time allocated to client work • Project Margin & Budget Adherence: ensure work is delivered profitable and within scope • Client Satisfaction / NPS: high feedback scores from clients on quality, outcomes, trust • Delivery Quality / Defect Rates: few production failures, strong monitoring, low rework • Innovation Output: internal tools, reusable components, AI accelerators shipped • Practice Growth: head
Apply tot his job
Apply To this Job