Data Science Research Manager - Accenture Research

Accenture•New York, NY

8d•$80,400 - $266,300•Hybrid

About The Position

We are Accenture Research, a team specialized in delivering impact through thought leadership and client insights. Our AI & Data Science team sits at the frontier of applied AI, combining rigorous quantitative methods with the latest in generative AI, agentic systems, and synthetic data generation to deliver differentiated intelligence for Accenture’s clients and internal stakeholders. We operate like a research lab with the accountability of a consulting team. Our work spans industries, geographies, and problem types — and we build the tools and methodologies that power it. We partner with world-class institutions including MIT Sloan and The Wharton School, and we invest in long-horizon research alongside immediate client impact. If you want to do the most technically ambitious, strategically relevant work of your career — in an environment that values rigor, craft, and intellectual honesty — this is the team. This role sits within Accenture Research’s AI team— a team built to develop next-generation research capabilities. We operate at the frontier of applied AI, combining rigorous quantitative methods with the latest in generative AI, agentic systems, and synthetic data generation to deliver differentiated intelligence for Accenture’s clients and internal stakeholders.

Requirements

Minimum of 5 years delivering analytical outputs in client-facing or commercial settings, with demonstrated ability to translate technical findings for executive audiences.
Minimum of 5 years applying machine learning methods, including simulations, supervised/unsupervised models, NLP, and time series.
Minimum of 2 years of experience with generative AI — including LLM prompting strategies, retrieval-augmented generation (RAG), and multi-modal models.
Minimum of 2 years of hands-on experience designing and building agentic AI architectures, including tool-use patterns, planning loops, and multi-agent orchestration.
Bachelor’s degree in Data Analytics, Data Science, Strategy, Economics, or a related field with minimum 5 years of work experience.
Master’s degree with minimum 5 years of work experience.
Python (advanced): modeling, data wrangling, pipeline development, and API integration.
Machine learning: supervised and unsupervised methods, ensemble models, time series, NLP, and text analytics.
Generative AI: LLM prompting, fine-tuning, retrieval-augmented generation (RAG), and multi-modal models.
Agentic AI: experience building agent architectures including tool use, planning loops, and multi-agent orchestration.
Synthetic data generation: methods such as SDV, Gaussian Copula Synthesizer, IPF calibration, or equivalent.
Google Cloud Platform (GCP) — critical requirement: Cloud Run, BigQuery, Vertex AI, Secret Manager, and GCP deployment architecture.
Strong business acumen: ability to contextualize analytical findings within industry dynamics and C-suite decision-making.
Excellent communication skills — written, verbal, and visual — for presenting to executive and non-technical audiences.
Strategic problem-solving mindset: comfortable moving from ambiguous business context to a structured analytical approach.
Strong project and stakeholder management capabilities in fast-paced, global environments.
Enthusiasm for cross-functional, multicultural teamwork with a bias toward building durable, reusable infrastructure.

Nice To Haves

PhD is a plus.
Experience designing and facilitating client workshops, co-creation sessions, or executive briefings in a consulting or advisory context.
Published thought leadership — white papers, industry reports, or HBR-style research — with demonstrated ability to synthesize complex AI topics for non-technical audiences.
Deep vertical expertise in at least one industry (e.g., financial services, consumer goods, healthcare, energy) with a track record of designing industry-specific AI solutions.
Experience with synthetic data generation methods (SDV, Gaussian Copula Synthesizer, IPF calibration, or equivalent) in research or commercial settings.
Background in academic or institutional research collaborations, such as with business schools or think tanks.

Responsibilities

Translate complex business problems into well-scoped, researchable analytical questions with clearly defined outputs and success metrics
Collaborate with research leads, economists, and client-facing teams to embed AI-native tools into project delivery
Design, build, and deliver ML and GenAI-powered analytical methodologies to support client engagements across multiple industries
Develop and iterate synthetic persona generation pipelines, including large-scale digital executive personas and behavioral simulation models
Implement agentic AI workflows using frameworks such as Google ADK, A2A, and MCP protocols on GCP
Design and maintain real-time intelligence dashboards and AI-as-a-service analytical assets
Coach and mentor junior data scientists, fostering a culture of technical rigor and business relevance