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Strategic Project Lead – Finance

AI Summary
Appen is seeking a Strategic Project Lead to deliver finance-focused AI pilots and convert successful experiments into scalable operational programs. The role combines project leadership with expertise in financial data, markets, accounting, quantitative models, and quality standards. The hire will partner with customers, researchers, and global delivery teams to define workflows, expert requirements, feasibility, and costs. Success requires comfort operating in a high-intensity environment while translating emerging financial AI use cases into reliable execution. Experience with AI training, model evaluation, data pipelines, or human-in-the-loop operations is advantageous.
Remote from
USA
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
6 Dec 2026
Listing views
103
Application actions
7
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Opportunity details

About this role.

Appen’s Strategic Project Management team is central to the company’s growth strategy, partnering with leading AI companies and frontier model developers to deliver the data that makes models work. As Strategic Project Lead – Finance, you will run strategic finance pilots and research projects for financial AI model training and evaluation, from initial scoping through to operationalization. This is a hands-on role bridging customer objectives, financial expertise, and Appen’s global delivery capability.

Your Impact

You’ll own the success of frontier financial AI pilots, turning emerging use cases into validated programs that meet defined standards for financial quality, feasibility, and cost.

You will shape how market data, financial statements, filings, disclosures, and quantitative and risk models are used for model training and evaluation.

This role is responsible for turning a pilot into a scalable project including workflows, expert requirements, and quality standards, developed with researchers and Appen’s global delivery teams.

What You Bring

  • Strong background in finance, financial engineering, economics, or accounting.
  • 2+ years leading complex projects, operational workflows or finance initiatives.
  • Depth in financial data, markets, accounting, or modeling, and the ability to judge when a dataset is fit for purpose.
  • Comfort working at pace in a high-intensity environment with an entrepreneurial approach.

Nice to Have

  • Experience with AI or ML companies, AI labs, or data annotation and evaluation projects.
  • Experience managing finance-focused, technical, or data-intensive projects.
  • Familiarity with data pipelines, AI training workflows, model evaluation, or human-in-the-loop operations.

Why You’ll Love Working Here

At Appen, we foster a culture of innovation, collaboration, and excellence. We value curiosity, accountability, and a commitment to delivering the highest quality AI solutions for frontier models.

You’ll work on complex challenges that shape the future of AI across industries and geographies, alongside talented people in a culture that values humility over ego. You’ll have the flexibility to deliver in a way that works for you and your team, supported by tools, resources, and development opportunities to continue to build your capability over time.

About Appen

Appen has been a leader in AI training data for over 30 years. We specialise in human generated data to train, fine tune, and evaluate models across generative AI, large language models, computer vision, and speech recognition. Our AI assisted data annotation platform and global crowd of more than 1 million contributors in over 200 countries support model pre training, supervised fine tuning, evaluation and benchmarking, safety and red teaming, and multilingual global expansion.

Compensation

Additional Information

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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThe role requires both domain judgment in finance and end-to-end leadership of ambiguous AI pilot programs. It involves aligning technical, customer, operational, quality, and cost considerations under fast-moving conditions.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$125,000
US market range (per year)$105k–$150k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer base pay was disclosed. The annual salary period is inferred from the full-time US professional role, and the figures are estimated USD annual market compensation for a finance-focused strategic project lead working on AI programs.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you scope a new finance-focused AI pilot with an ambiguous customer request?

I would begin by defining the intended model use case, target users, success metrics, data constraints, and risk boundaries. I would then turn these inputs into a pilot charter covering datasets, expert profiles, workflow stages, quality thresholds, timeline, cost assumptions, and decision gates.

How do you determine whether a financial dataset is fit for AI training or evaluation?

I assess provenance, licensing, completeness, timeliness, schema consistency, representativeness, bias, and the relevance of labels or annotations to the intended task. I would also use a sample-based review with finance experts and define measurable acceptance criteria before scaling production.

Describe how you would convert a successful pilot into a scalable program.

I would document the validated workflow, establish repeatable operating procedures, define staffing and expert qualification requirements, instrument quality and throughput metrics, and identify automation opportunities. A controlled ramp with regular calibration sessions would confirm that quality and economics remain stable at scale.

How would you manage disagreements between a customer, researchers, and delivery teams about quality requirements?

I would make the trade-offs explicit through shared metrics, examples, and cost or timeline scenarios. My goal would be to align stakeholders on a minimum acceptable quality bar, escalation process, and evidence-based decision framework rather than relying on subjective preferences.

What risks are especially important when using financial content in AI projects?

Key risks include inaccurate or stale data, licensing restrictions, confidential information, misleading labels, unsuitable use of forward-looking statements, and insufficient expert review. I would address these through data governance, access controls, documented validation, domain-expert calibration, and ongoing quality audits.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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