Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Strategic Project Lead – Finance
- Remote from
- USA
- Salary
- Undisclosed
- Department
- Project & Program Management
- Employment
- Full Time
- Experience
- Senior
- Published
- Apply before
- 6 Dec 2026
- Listing views
- 84
- Application actions
- 5
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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
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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Role DNA
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Core skills
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Sample interview questions
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.
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.
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.
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.
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.
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