Chief Data Scientist

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

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