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NTT DATA AIVista

Runs governance-driven enterprise AI workloads

Senior Financial Analyst - FP&A and Finance Operations

Full-TimeUpdated on 10/15/2026
$120k - $170k/yr
Senior
Palo Alto, CA, USA+1 moreMore locations: San Francisco, CA, USA
Hybrid
Finance & BankingCorporate Finance & FP&A

About the job

Requirements
  • At least 5 years of experience in FP&A, finance operations, corporate finance, or a related role.
  • Strong foundation in budgeting, forecasting, variance analysis, financial modeling, and management reporting.
  • Demonstrated experience owning recurring finance processes and managing financial information accurately and attentively.
  • Experience administering finance systems, expense management platforms, or other financial tools.
  • Familiarity with payroll processes, including payroll inputs, validation, coordination, and recurring processing requirements.
  • Advanced Excel or Google Sheets skills and ability to work with financial and operational data from multiple sources.
  • Ability to investigate variances, identify trends, and explain financial results.
  • Ability to independently manage multiple recurring processes, deadlines, and priorities.
  • Comfort working directly with senior executives and taking ownership of analytical and operational responsibilities.
  • Strong judgment, discretion, and attention to detail when handling sensitive financial and employee information.
  • Strong written and verbal communication skills.
Responsibilities
  • Own and maintain annual budgets, forecasts, and financial planning models.
  • Analyze actual performance against budget and forecast, identify key variances, and explain underlying business drivers.
  • Develop and maintain financial models supporting operating plans, headcount planning, spending decisions, and resource allocation.
  • Partner with functional leaders to understand spending, financial assumptions, and evolving business needs.
  • Prepare scenario and sensitivity analyses to support planning and decision-making.
  • Track financial performance and identify emerging trends, risks, and opportunities.
  • Improve forecasting accuracy, planning processes, and financial visibility as the company scales.
  • Own and coordinate day-to-day finance processes, ensuring accurate, consistent, and timely execution.
  • Administer and maintain finance systems, including Ramp and other financial management tools.
  • Maintain accurate financial and operational records, including finance systems of record, trackers, and supporting documentation.
  • Coordinate and execute recurring payroll processes, including maintaining accurate inputs, validating changes, and ensuring timely processing.
  • Manage expense management processes, spending controls, approval workflows, and related finance administration.
  • Coordinate with internal stakeholders and external accounting, payroll, and finance partners to ensure accurate information and smooth processes.
  • Maintain recurring finance calendars, reporting schedules, and key deadlines.
  • Identify opportunities to streamline, automate, and improve finance workflows, systems, and internal controls.
  • Own recurring operational finance activities and escalate exceptions or issues to the CFO as appropriate.
  • Support ad hoc operational projects and programs as needed.
  • Prepare recurring monthly and quarterly financial reporting packages for CFO review.
  • Consolidate financial and operational data from multiple sources to provide an accurate view of company performance.
  • Develop and maintain dashboards, trackers, and reports covering financial performance, spending, headcount, and other key business metrics.
  • Support executive and Board-level financial reporting and materials.
  • Investigate financial variances, identify underlying drivers, and provide clear explanations and recommendations.
  • Support the CFO with financial analyses related to hiring, investments, resource allocation, and other business decisions.
  • Respond to ad hoc financial and operational questions with timely, accurate analysis.
  • Use AI, automation, and modern analytical tools to improve the efficiency and quality of financial processes, reporting, and analysis.
Desired Qualifications
  • Experience in a startup, high-growth technology company, or similarly fast-moving environment.
  • Experience supporting a CFO or senior Finance leader in a lean finance organization.
  • Hands-on experience with Ramp or similar expense management and finance platforms.
  • Experience coordinating payroll and working with external payroll, accounting, or finance service providers.
  • Experience building or improving FP&A models, financial reporting, dashboards, and recurring finance processes.
  • Experience supporting executive and Board-level financial reporting.
  • Experience with headcount planning, workforce costs, spending analysis, and resource allocation.
  • Familiarity with financial systems, reporting tools, SQL, BI platforms, or similar technologies.
  • Experience implementing process improvements, automation, and financial controls.
  • Interest in using AI and automation to improve Finance workflows and reporting.

About the company

NTT DATA AIVista is a subsidiary of NTT DATA that helps large enterprises deploy AI in regulated environments by turning complex workflows into agentic AI systems through a Service-As-Software platform. It uses last-mile AI specialization to tailor foundation models to an organization’s specific domain, governance, and policies, enabling native workflow integration, deep domain expertise, and enterprise-grade governance to run AI safely and at scale. The platform pieces together AI software with the company’s existing processes and systems, leveraging NTT DATA’s industry knowledge and systems integration experience. The goal is to accelerate enterprise AI adoption, reduce costs, and improve reliability by operating within regulatory and policy constraints while delivering faster outcomes in mission-critical workflows such as finance and healthcare.

Company Size

11-50

Company Stage

N/A

Total Funding

N/A

Headquarters

N/A

Founded

2025

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Simplify's Take

What believers are saying

  • NTT DATA launched AI for Insurance on August 5, 2026, showing productization.
  • The company says it won orders from multiple European and U.S. insurers.
  • NTT DATA targets FY2026 rollout from AIVista's core platform into projects.

What critics are saying

  • NTT DATA still depends on parent integration; AIVista lacks proven standalone demand.
  • Competitors like Snowflake and hyperscalers own governance, data, and distribution layers.
  • If AI for Insurance misses revenue targets, AIVista becomes another services wrapper.

What makes NTT DATA AIVista unique

  • AIVista launched in December 2025 to industrialize regulated-industry agentic AI.
  • August 2026 AI for Insurance packages governance, workflow orchestration, and model-routing.
  • Bratin Saha brings AWS SageMaker, Amazon Q, and NVIDIA platform-building experience.

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Benefits

Health Insurance

Dental Insurance

Vision Insurance

401(k) Retirement Plan

Health Savings Account/Flexible Spending Account

Paid Holidays

Hybrid Work Options

Company News

NOVALOGIQ
Jul 30th, 2026
NTT DATA AIVista and Snowflake: Identity alone won't secure enterprise AI agents.

NTT DATA AIVista and Snowflake: Identity alone won't secure enterprise AI agents. Presented by NTT DATA AIVista VentureBeat's June research found that 69% of enterprises are still running AI agents that share credentials, a practice associated with higher rates of security incidents and near-incidents. But at VB Transform 2026, Mukesh Karki, CTO of NTT DATA AIVista, and Mayank Upadhyay, chief security and trust officer at Snowflake, argued that fixing identity is only the first step. Enterprises also need action-level authorization and tamper-resistant audit trails built into every agent interaction if they're going to deploy autonomous systems safely at scale. "These organizations need to be able to prove to their auditors in a very tamper-resistant fashion that those records showing what they did actually prove what they're doing," Karki said. "And the provability is essentially your license to operate in a regulatory environment." Why shared credentials cause agentic AI security incidents. The problem, Upadhyay says, is many assumptions were carried over from an earlier generation of software. "In the traditional software world, a human being clicks somewhere and the software does something very deterministic, and you know which API it's going to call," he said. "But in the agentic world, the software has a brain of its own, and it's constantly rewiring itself. If you give this software more permission than it needs for a particular goal, agents are exploratory by nature, so they're going to try lots of different things, and you'll have unintended side effects." Embedding a single static API key compounds the exposure, he added. "It's a really bad pattern if you have one API key, you shove it into the agent, and it's talking as anybody to a particular SaaS service, because then you're giving this agent the union of everybody's needs," he said, noting that the second failure mode is forensic, since "things may go wrong, and you wouldn't be able to attribute it to the right agent." Scoped credentials are only the starting point in regulated industries. Karki, whose clients are mostly in insurance, healthcare, and finance, treats scoped credentials as table stakes. "In a regulatory setting, an agent that's not broadly scoped with shared scope credentials is not going to run, period," Karki said. "Having a scope credential is just a starting point. There are actually two layered constraints. One is the jurisdiction in which the agent operates, and then it's the jurisdiction or the rules of that organization." For instance, a claims adjustment agent in Washington State operates on different regulations than one in California, he adds, and every claim is different. "Those scoped credentials are not enough, because it has to be action-based and rules-based at the time it's taking action," he added. Where the employee analogy for AI agents breaks down. The employee analogy, Karki argued, only goes so far. Agents still need to learn an organization's unique context, much as a new employee does. But unlike people, enterprises can't realistically build trust with thousands of agents over time. "A star employee in one organization might not be the best employee when they move to a different organization, not because they became worse, but because they don't have the context of this new place, and the same is true with agents," Karki said. "If every employee has 100 agents, you can't say you're going to onboard these agents and do a background check on them." Upadhyay said the employee analogy should place agents one rung lower in the organizational hierarchy. "Treat them like interns," he suggested. "They have good intent, but they don't always know what they're doing, and you have to keep your eye on them while you gradually build trust." On the Snowflake platform, administrators can impose platform-wide guardrails such as read-only operations, while developers further narrow an agent's permissions when they launch each session. A three-layer approach to AI agent governance. There's no question where governance belongs, Karki says. "Governance has to happen at every agent action, and it has to sit outside the agent," he explained. "That's the only way you'll be able to prove later that the agent took an action it was allowed to take." Upadhyay broke governance into three layers: The agent layer covers identity, tool permissions, and MCP governance. The model layer addresses indirect prompt injection and enables models to run inside the customer's VPC so prompts remain invisible to the model provider. The data layer covers least-privilege access, zero-copy architecture, and role-based access control. For agents to work properly, governance is required across all three. What enterprises should audit first. For enterprises auditing the governance of existing AI agents, Upadhyay recommends starting in two places. The first is auditing permissions for static secrets, the largest fixable attack vector. Next is addressing shadow AI through an MCP gateway, so developers no longer have to run bootlegged open-source MCP servers under their desks and administrators have visibility into who's talking to which MCP server. There's a tradeoff between constraint and capability, and that can be addressed at the task level, with confidence scoring used to withhold autonomous execution on high-risk actions, and sandboxing as a middle path. But Karki cautions enterprises already scaling their agentic systems. "A lot of this can't be retrofitted after you have an agentic system running, and it's even harder to retrofit if you have to prove to your auditors why exactly the agent behaved the way it did," he explained. "Provability has to be built ground up when you're designing the system." Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they're always clearly marked. For more information, contact [email protected].