Strategic Account Manager
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Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.
What You’ll Own:
- Coordinate with internal teams, such as data engineers, analysts, and project managers, to ensure timely and successful delivery of client projects;
- Monitor project progress, identify potential risks or issues, and proactively address them to ensure client satisfaction;
- Manage project scope, timelines, and budgets, and communicates any changes or updates to clients promptly;
- Serve as the primary point of contact for assigned accounts, managing client relationships, and ensuring high levels of client satisfaction;
- Conduct regular client meetings, presentations, and status updates to ensure effective communication and project progress.
- Architect cross-functional delivery plans with technical and operations teams, managing risk and iteration at scale;
- Present insights and program expansion strategies to product, safety, and procurement leads to client;
- Shape roadmap of emerging services based on client need and delivery capability;
- Develop and implement account plans to ensure client retention and long-term partnerships.
Minimum Requirements:
- Master’s degree in Business Administration, Marketing or a closely related field and 2 years of progressive, post-baccalaureate experience in any related occupation.
- Must have 2 years of experience in managing senior level client engagements end-to-end from strategy design through execution, to align strategic priorities with operational delivery.
- Must have 2 years of experience in crafting strategies for operational or technology-driven initiatives, using both quantitative and qualitative research methods. Any required experience may have been gained concurrently.
The salary for this position is $183,102 USD per year.
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View Company ProfileInnodata is an elite, global data engineering and AI enablement powerhouse engineered to orchestrate massive-scale high-quality data operations, algorithmic training datasets, and digital transformation workflows for the world’s largest technology companies and enterprises. Operating as a critical "intelligence infrastructure layer" for the modern AI economy, the company eliminates the operational friction of deploying complex LLMs and generative AI applications—which frequently suffer from low-quality training data, biased outputs, and fragmented annotation pipelines—by seamlessly deploying a combination of advanced proprietary data annotation platforms, automated synthetic data generation, and an elite global network of subject matter experts. Moving beyond basic crowdsourced data validation paradigms, Innodata empowers Fortune 500 enterprises, global legal publishers, and leading medical institutions to dynamically scale their core AI foundation models, custom machine learning pipelines, and multi-modal semantic data processing with elite, scalable, and audit-ready precision. Under the hood, their sophisticated operational framework—bolstered by strict data security compliance, persistent programmatic data quality controls, and deep domain expertise across vertical domains like legal, healthcare, and finance—natively manages high-velocity data curation, complex enterprise knowledge graph construction, and high-stakes model evaluation. What sets Innodata apart is its uncompromising dedication to purpose-driven data craftsmanship; by bridging the gap between raw, unstructured enterprise information and high-performance, fine-tuned AI execution, the firm enables global commercial organizations to radically accelerate their AI time-to-market, eliminate systemic data engineering bottlenecks, and build an unassailable foundation for continuous commercial growth in the modern, AI-transformed global marketplace.
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