Data and AI : Go-to-Market - Director (Greater New York)

Who You’ll Work With

At Slalom, we combine personal connection with global scale to create environments where work is loved, and life is enriched. Our mission is to help organizations redefine possibilities, shape the future, and achieve transformative goals. This position reports to the Greater New York Go-to-Market Data Lead and is based in NYC, where you’ll play a pivotal role in driving Data and AI sales and fostering customer-centric go-to-market strategies within the Financial Services industry vertical

What You’ll Do

Strategic Solutioning: Develop compelling proposals, presentations, and statements of work (SOW) to drive business growth. Identify opportunities for Slalom in data and AI, both in existing and new environments. Lead discovery and solution design conversations with Financial Services clients, translating data and AI capability into clear business outcomes and commercial proposals.

Financial Services Market Development: Build and grow Slalom’s Data & AI pipeline and revenue within Banking, Capital Markets, Insurance, and Asset Management. Leverage existing relationships and develop new executive connections across the financial services sector in the Greater New York market.

Origination & Offering Development: Recognize whitespace in the FS market, shape ideas into sellable offerings, mobilize the right Slalom capabilities, and help close the work — turning market insight into pipeline and revenue, not just managing delivery.

Subject Matter Expertise: As a senior leader, engage with clients, influence executives, and drive customer-focused solutions. Leverage your deep understanding of data and intelligence to deliver value. Demonstrate fluency in Financial Services data challenges — regulatory data management (Basel III/IV, CCAR, DFAST), risk data aggregation, client 360, and AI-driven automation in financial workflows.

Platform & Technology Fluency: Speak credibly about modern data and AI platforms — Databricks (Lakehouse, Unity Catalog, Mosaic AI), Snowflake (Data Cloud, Cortex AI), and cloud-native architectures on Azure, AWS, and GCP. Able to engage technically with data architects and engineers while translating platform capability into business value for executives.

AI Transformation Leadership: Lead AI-forward client conversations across the adoption curve — from AI Readiness assessments and governed data foundations, to agentic AI workflows, LLM/GenAI applications, and operationalized AI at scale. Comfortable discussing responsible AI, model governance, and AI ROI with C-suite audiences.

Data & AI Thought Leader: Serve as a data and AI thought leader by speaking at external events and partnering with marketing to create and lead thought leadership events. Actively represent Slalom at Financial Services industry forums, roundtables, and partner events (e.g., Databricks FSI Summit, Snowflake Financial Services events).

Expected Revenue Impact: $2.5M+ personal contributions and a $5M+ portfolio of business in the market.

Utilization: 45%–65% (*varies based on Senior Principal/Director level)

What You’ll Bring

Experience & Leadership

• 10+ years in consulting leadership, with at least 5+ years specializing in Data, Analytics, or AI.

• 5+ years of hands-on experience engaging Financial Services clients (banking, capital markets, insurance, asset management) — understanding their regulatory environment, data architecture challenges, and AI adoption maturity. Tier 1 / Tier 2 banking experience strongly preferred.

• Proven consulting growth and origination ability — a track record of creating and closing net-new opportunities, not merely delivery revenue under management.

• Technical depth to command the room with a bank CDO or Chief Data Architect, commercial instinct to drive the sale, and enough FS fluency to understand the client’s business without translation.

• 7–10 years of leading technical pursuits, with proven experience in technical solutioning, commercial strategy, and account growth.

• Track record of growing existing client relationships and expanding new accounts.

Financial Services Domain Knowledge

• Deep understanding of FSI data challenges: regulatory reporting (CCAR, DFAST, Basel III/IV, BCBS 239), risk data aggregation, KYC/AML data pipelines, client 360, and trade/transaction data.

• Familiarity with common FSI data architectures: enterprise data warehouses, risk data marts, cloud data lakes, and real-time data platforms for fraud detection an…