Associate Director

Acuity Analytics; Gurugram District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Acuity Analytics in India is seeking a solutions leader for Private Markets, bridging technology and human expertise to design client-focused outcomes. You will engage with clients, lead conversations, and work with PE, private credit, secondaries, and value creation teams to generate alpha.

You will translate investment needs into scalable data architectures, prototype dashboards within a week, and define scope for proposals.

Qualifications

• Provide real-time solution guidance during client meetings on feasibility, effort, sequencing and risk.
• Translate PE workflows (deal screening, IC memos, LP reporting, portfolio monitoring, ESG/regulatory reporting) into data architectures and AI use cases.
• Deliver rapid prototypes with data engineers to build sample data models, pipelines or dashboards within one week.
• Define scope with clients and produce scoped, priced proposals, SOWs and estimates combining delivery, engineering and AI.
• Clarify AI applicability; define where AI helps in memos, reporting, diligence, strategy research, and ensure explainable outputs.
• Develop reusable assets: reference architectures, demos, pricing models and case studies to improve repeat sales.
• Create clear handovers with client-validated briefs; remain domain owner through build.
• Support account pursuits with Directors and Sales on RFPs, demos and pilots for PE clients globally.
• Monitor PE data and AI tooling landscape (Snowflake, Databricks, dbt, Airflow, DealCloud, CRMs, LLMs) and competitor capabilities.
• Mentor analysts and help build a solutioning pod within the domain team.

Responsibilities

• Provide real-time solution guidance- Join client meetings with account directors and offer an informed first view on feasibility, effort, sequencing and risk for data, digital and AI requirements while the client is still shaping the need.
• Convert investment needs into solutions -Translate PE workflows such as deal screening, IC memo drafting, LP reporting, portfolio monitoring, ESG and regulatory reporting workflows. Additionally support workflow closed to Consulting and Corporate strategy needs such as market and competitive research, due diligence, financial modelling, benchmarking into target data architecture, AI use cases and delivery models, using the 7-step data journey as the guiding framework.
• Deliver rapid prototypes- Work with data engineers, prototypes and AD&D teams to build a sample data model, pipeline or dashboard using real client data within one week.
• Define scope before the RFP- Co-develop requirements with the client and produce scoped, priced proposals, SOWs and effort estimates that bring together domain delivery, managed services, engineering and AI under one accountability model.
• Clarify AI applicability - Clearly define what AI can and cannot support across IC memos, LP reporting and diligence, Strategy research, outside in analysis, strategic assessment workflow and design human-in-the-loop controls so outputs are decision-ready, explainable and traceable for Partners, LPs and auditors.
• Develop reusable solution assets - Maintain solution patterns, reference architectures, demo environments, pricing models and case studies to improve efficiency and reduce the effort required for repeat sales.
• Create clear engineering handovers - Provide engineering teams with client-validated briefs and remain the domain owner through build and managed run to ensure continuity from discussion to delivery.
• Support account pursuits - Partner with Directors and Sales on discovery calls, RFP responses, demos and pilots for existing PE clients and new logos across Europe and North America.
• Monitor market and technology trends -Track the PE data and AI tooling landscape, including Snowflake, Databricks, dbt, Airflow, data catalogues, DealCloud, CRMs, LLMs and agent platforms, while understanding competitor capabilities and client AI stacks.
• Coach and build capability -Mentor analysts on solution-led thinking and help establish a small solutioning pod within the domain team

Skills

Solution design

Client engagement

PE knowledge

Data architecture

AI applications

Cross-functional

Communication

Tools

Snowflake

Databricks

dbt

Airflow

Job description

Job Description:

Job Purpose

We are looking for a leader who can lead solutioning for our Private Markets segment, which includes services for corporates operating as portfolio companies owned by Private Equity firms. The role involves understanding client challenges, designing solutions that combine technology and human expertise, engaging directly with clients, leading conversations, and supporting the execution of the proposed solutions. You will serve as a solutions expert, working closely with domain teams specializing in Private Equity, Private Credit, Secondaries, and Value Creation initiatives aimed at generating alpha for Private Equity investments. You will act as the bridge between our clients, subject matter experts, and our data, engineering, and AI team.

Key Responsibilities: -

• Provide real-time solution guidance- Join client meetings with account directors and offer an informed first view on feasibility, effort, sequencing and risk for data, digital and AI requirements while the client is still shaping the need.
• Convert investment needs into solutions -Translate PE workflows such as deal screening, IC memo drafting, LP reporting, portfolio monitoring, ESG and regulatory reporting workflows. Additionally support workflow closed to Consulting and Corporate strategy needs such as market and competitive research, due diligence, financial modelling, benchmarking into target data architecture, AI use cases and delivery models, using the 7-step data journey as the guiding framework.
• Deliver rapid prototypes- Work with data engineers, prototypes and AD&D teams to build a sample data model, pipeline or dashboard using real client data within one week.
• Define scope before the RFP- Co-develop requirements with the client and produce scoped, priced proposals, SOWs and effort estimates that bring together domain delivery, managed services, engineering and AI under one accountability model.
• Clarify AI applicability - Clearly define what AI can and cannot support across IC memos, LP reporting and diligence, Strategy research, outside in analysis, strategic assessment workflow and design human-in-the-loop controls so outputs are decision-ready, explainable and traceable for Partners, LPs and auditors.
• Develop reusable solution assets - Maintain solution patterns, reference architectures, demo environments, pricing models and case studies to improve efficiency and reduce the effort required for repeat sales.
• Create clear engineering handovers - Provide engineering teams with client-validated briefs and remain the domain owner through build and managed run to ensure continuity from discussion to delivery.
• Support account pursuits - Partner with Directors and Sales on discovery calls, RFP responses, demos and pilots for existing PE clients and new logos across Europe and North America.
• Monitor market and technology trends -Track the PE data and AI tooling landscape, including Snowflake, Databricks, dbt, Airflow, data catalogues, DealCloud, CRMs, LLMs and agent platforms, while understanding competitor capabilities and client AI stacks.
• Coach and build capability -Mentor analysts on solution-led thinking and help establish a small solutioning pod within the domain team

Key Competencies: -

• Private Markets and Consulting expertise – Strong understanding of the PE deal lifecycle, including fundraising, origination, diligence, execution, portfolio monitoring, compliance and exits, along with key metrics such as IRR, MOIC, DPI, TVPI, EBITDA bridge and NRR. Along with this, background in Consulting assignments in digital transformation etc would be helpful. The potential candidate should be able to engage confidently with Partners, IR and operating teams.
• Data and analytics capability - Solid knowledge of data lakes, warehouses, pipelines, data modelling, taxonomy, metadata, lineage and BI. Comfortable reading schemas and SQL queries and translating architecture into simple visual concepts.
• Practical AI understanding- Working knowledge of LLMs, RAG, agents and document extraction, with the ability to explain where AI is useful, where it has limits and how it applies in client situations.
• Commercial orientation - Able to estimate effort, shape pricing and write clear proposals, including models beyond FTE-based pricing such as outcome-based, platform-plus-service and managed-run structures.
• Clear communication - Uses plain language, structured thinking, strong slides and crisp written proposals, with credibility in front of senior client stakeholders.
• Collaborative execution - Works effectively across domain, engineering, AI and sales teams, influencing outcomes without relying on direct authority.
• Action bias - Prioritizes prototypes over lengthy presentations and is comfortable working through ambiguity under tight timelines.
• Knowledge of AI capabilities and practical business applications is a must.