Principal Data & AI Consultant

We need to output HTML fragment with sections: headings

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must include only sections that have real specific content. The posting includes a description: "Technology is our how..." then "Job Description" then bullet-like paragraphs. We need to parse content. We need to avoid repeating job title (not given). No Job ID. Remove boilerplate: EEO, legal, etc. Also remove "Additional Information" about confidentiality, etc. Also remove "Additional Employee Requirements" (physical requirements) maybe considered boilerplate? It's not about role but generic physical requirements; likely remove. Also remove benefits section? There's "USA Benefits (Full time roles only...)" which includes benefits details. That is specific content about benefits, so we should include a Benefits section with that content. Pay and Schedule not mentioned, so omit those sections. We need sections: About the role (maybe the introductory paragraph before Job Description). Responsibilities (the job description bullet points). Qualifications (the Required and Preferred sections). Skills maybe could be part of Qualifications but we could separate Skills if there is explicit skills list. The posting includes many skill requirements; we could put them under Qualifications or Skills. The instruction: Use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We can choose which to include. Likely include About the role, Responsibilities, Qualifications, Benefits. Possibly Skills separate but we can incorporate into Qualifications. To follow instruction, we could have Qualifications and Skills separate if there is distinct content. The posting lists qualifications (required, preferred) and also skills (Python, SQL, etc). Might be better to have Qualifications (required/preferred) and Skills (technical). But we can also put all under Qualifications. Safer to follow typical: About the role, Responsibilities, Qualifications, Benefits. We'll omit Skills if not separate. We must preserve ALL substantive information; do not invent or summarize away detail. So we need to keep all details from the posting, except boilerplate removed. Let's extract: Intro paragraph before "Job Description": "Technology is our how. And people are our why. For over two decades, we have been harnessing technology to drive meaningful change. By combining world-class engineering, industry expertise and a people-centric mindset, we consult and partner with leading brands from various industries to create dynamic platforms and intelligent digital experiences that drive innovation and transform businesses. From prototype to real-world impact - be part of a global shift by doing work that matters." That's About the role. Then "Job Description" heading (we should not repeat as heading? The instruction: Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

or drop it. It doesn't say we cannot use a heading for Job Description; but we should use headings for sections like About the role, Responsibilities, etc. So we can treat the content after Job Description as Responsibilities. Thus Responsibilities content: list of duties: - Serve as a senior trusted advisor to CIO, CTO, CDO, CRO, business, product, and technology stakeholders. - Lead Data & AI discovery and translate ambiguous business problems into measurable, production-ready solutions. - Design end-to-end architectures spanning data platforms, analytics, machine learning, GenAI, applications, APIs, governance, and integrations. - Lead and challenge advanced forecasting and predictive-modeling approaches, including regression, sparse and zero-inflated data, feature design, model selection, tuning, and validation. - Define appropriate business and model success measures, including R², WAPE, MAPE, statistical significance, and business-impact KPIs. - Ensure point-in-time correctness, prevent data leakage, and maintain rigorous model-development and validation practices. - Provide hands-on technical leadership using Python, SQL, Snowflake, notebooks, Git, and modern Data/AI platforms. - Shape AI use cases across forecasting, sponsorship sales, lead scoring, next-best-action, revenue intelligence, personalization, subscription growth, and commercial optimization. - Evaluate when classical analytics/ML, GenAI, or agentic AI is the appropriate solution. - Lead architecture and solution-design workshops and present recommendations to senior and executive audiences. - Support proposals, SOWs, RFI/RFP responses, estimates, staffing models, and technical solution shaping. - Lead and mentor multidisciplinary teams across Data Science, Data Engineering, AI Engineering, Software Engineering, and Architecture. - Actively participate in client stand-ups, backlog refinement, executive readouts, and strategic planning. - Teach and transfer knowledge to client teams; documentation, reproducibility, and handover are expected parts of delivery. - Challenge client requests when the proposed approach does not solve the underlying business problem. We need to put these in

• . Then Qualifications: Required and Preferred sections. Required: - 7+ years of applied forecasting and data domain experience. - Proven experience leading at least two comparable forecasting engagements from discovery through production/handover. - Strong experience with comparable-unit / same-store-style forecasting approaches. - Expert-level multivariable regression, collinearity analysis, VIF interpretation, model tuning, selection, and holdout validation. - Strong understanding of sparse and zero-inflated datasets; must understand why nulls cannot be silently treated as zero. - Strong metric fluency, including R², WAPE, MAPE, and p-values, with the ability to explain metric selection in business language. - Deep understanding of data leakage and point-in-time feature correctness. - Strong Python and SQL skills and experience creating reproducible analytical workflows/notebooks. - Git and pull-request-based software delivery experience. - Strong Data Architecture and Solution Architecture capability across ingestion, transformation, modeling, security, governance, APIs, cloud, and production operations. - Strong understanding of ML, GenAI/LLMs, RAG, AI agents, and modern enterprise AI patterns. - Proven executive communication skills; must be able to present to non-statistical audiences using business outcomes first and methodology second. - Must be capable of explicitly communicating forecast/model limitations, uncertainty, and risk in plain language. - Proven client consulting and thought-leadership experience; able to teach methodology, not simply execute it. - Demonstrated experience leading senior client workshops, technical discovery, architecture discussions, and executive readouts. - Strong commercial judgment and ability to connect technical decisions to revenue, operational, or customer outcomes. - Preferred domain experience in sports, media, entertainment, streaming, ticketing, subscriptions, sponsorship, advertising, telecom, or commercial/revenue analytics. Preferred (Desired): - Hierarchical/mixed-effects modeling, ADRs, large-scale categorical encoding, MLOps, governance, and responsible AI experience. - Strong Snowflake experience; Snowflake ML / Model Registry experience. - 10–12+ years overall Data, Analytics, AI, Architecture, or related technology experience. We could also include "Desired:" as part of Preferred. We need to preserve all details. Then Benefits: from "USA Benefits (Full time roles only, does not apply to contractor positions)" paragraph. Content: - Robust healthcare and benefits including Medical, Dental, vision, Disability coverage, and various other benefit options - Flexible Spending Accounts (Medical, Transit, and Dependent Care) - Employer Paid Life Insurance and AD&D Coverages - Health Savings account paired with our low-cost High Deductible Medical Plan - 401(k) Safe Harbor Retirement plan with employer match with immediately vest Also there is a sentence about At Endava, we’re committed... that's EEO boilerplate; we should remove that as it's EEO/equal-opportunity boilerplate. Also the lines about hiring decisions based on merit etc. Remove. Also remove "Additional Information" about confidentiality, EEO guidelines. Remove "Additional Employee Requirements" (physical requirements). Remove the lines about accommodations etc? Those are also boilerplate; but they might be considered EEO/legal. Probably remove. Thus final HTML:

  About the role

  Technology is our how. And people are our why. For over two decades, we have been harnessing technology to drive meaningful change. By combining world-class engineering, industry expertise and a people-centric mindset, we consult and partner with leading brands from various industries to create dynamic platforms and intelligent digital experiences that drive innovation and transform businesses. From prototype to real-world impact - be part of a global shift by doing work that matters.

  Responsibilities

  • Serve as a senior trusted advisor to CIO, CTO, CDO, CRO, business, product, and technology stakeholders.
  • Lead Data & AI discovery and translate ambiguous business problems into measurable, production-ready solutions.
  • Design end-to-end architectures spanning data platforms, analytics, machine learning, GenAI, applications, APIs, governance, and integrations.
  • Lead and challenge advanced forecasting and predictive-modeling approaches, including regression, sparse and zero-inflated data, feature design, model selection, tuning, and validation.
  • Define appropriate business and model success measures, including R², WAPE, MAPE, statistical significance, and business-impact KPIs.
  • Ensure point-in-time correctness, prevent data leakage, and maintain rigorous model-development and validation practices.
  • Provide hands-on technical leadership using Python, SQL, Snowflake, notebooks, Git, and modern Data/AI platforms.
  • Shape AI use cases across forecasting, sponsorship sales, lead scoring, next-best-action, revenue intelligence, personalization, subscription growth, and commercial optimization.
  • Evaluate when classical analytics/ML, GenAI, or agentic AI is the appropriate solution.
  • Lead architecture and solution-design workshops and present recommendations to senior and executive audiences.
  • Support proposals, SOWs, RFI/RFP responses, estimates, staffing models, and technical solution shaping.
  • Lead and mentor multidisciplinary teams across Data Science, Data Engineering, AI Engineering, Software Engineering, and Architecture.
  • Actively participate in client stand-ups, backlog refinement, executive readouts, and strategic planning.
  • Teach and transfer knowledge to client teams; documentation, reproducibility, and handover are expected parts of delivery.
  • Challenge client requests when the proposed approach does not solve the underlying business problem.

  Qualifications

  • 7+ years of applied forecasting and data domain experience.
  • Proven experience leading at least two comparable forecasting engagements from discovery through production/handover.
  • Strong experience with comparable-unit / same-store-style forecasting approaches.
  • Expert-level multivariable regression, collinearity analysis, VIF interpretation, model tuning, selection, and holdout validation.
  • Strong understanding of sparse and zero-inflated datasets; must understand why nulls cannot be silently treated as zero.
  • Strong metric fluency, including R², WAPE, MAPE, and p-values, with the ability to explain metric selection in business language.
  • Deep understanding of data leakage and point-in-time feature correctness.
  • Strong Python and SQL skills and experience creating reproducible analytical workflows/notebooks.
  • Git and pull-request-based software delivery experience.
  • Strong Data Architecture and Solution Architecture capability across ingestion, transformation, modeling, security, governance, APIs, cloud, and production operations.
  • Strong understanding of ML, GenAI/LLMs, RAG, AI agents, and modern enterprise AI patterns.
  • Proven executive communication skills; must be able to present to non-statistical audiences using business outcomes first and methodology second.
  • Must be capable of explicitly communicating forecast/model limitations, uncertainty, and risk in plain language.
  • Proven client consulting and thought-leadership experience; able to teach methodology, not simply execute it.
  • Demonstrated experience leading senior client workshops