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Dataiku
AI & Machine Learning 4h ago

Technical Account Manager

Dataiku
United StatesUnited States
Full-time
$150,000 — $200,000 USD
Senior-Level

Job Description

Key Skills Required

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ML OpsAWSKubernetes

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Technical Account Managers are highly experienced Architects who are comfortable with a very client facing role and who dedicate themselves to a small set of strategic clients, (~4 per TAM). They must be quick on their feet and able to put a positive spin on challenging customer situations, both in the boardroom with a client CTO and while sharing the command line with a client admin. They must be effective technically, both as communicators and doers. They must be capable of managing and maintaining a client relationship, while keeping a tight organizational watch over the technical aspects of their accounts.

How you'll make an impact

  • Work with the Customer Success team and customers to jointly identify near and longer-term priorities and define the associated engagement plan

  • Manage milestones with Customer Success Manager and customer and contribute to deliverables, provide regular status updates and proactively identify and mitigate issues/risks

  • Centralize the technical information about clients and take part to the account strategy and with the other members of the account team

  • Play an active role contributing to the growth and scalability of the Field Engineering team through robust documentation, continuous process optimization, and peers upskilling

  • Work with Customer resources as a primary technical advisor, providing guidance and hands-on support on the following matters: Dataiku platform architecture (initial deployment, expansions), platform operations (upgrades), best practice related to Dataiku usage, security, data management, compute resources, ML-Ops, Monitoring, etc.

  • Help clients troubleshoot the implementation of the product within their systems.

  • Ensure that feature requests are effectively recorded and communicated to product and R&D

  • Advise client tech leaders on choices around new companion technologies and tech strategies around Dataiku

  • Support innovative approaches around Dataiku (edge computing, deep learning, advanced MLOps, for example)

What you'll need to be successful

  • 7+ years of experience in a customer facing technical role

  • Comfort and confidence in client-facing interactions

  • Ability to work both pre and post sale

  • Strong Linux system administration experience including networking

  • Experience with authentication and authorization systems like LDAP, Kerberos, AD, and IAM

  • Hands-on experience with cloud based services like AWS, Azure and GCP

  • Hands-on experience with the Kubernetes ecosystem for setup, administration, troubleshooting and tuning

  • Experience with the Hadoop and/or Spark ecosystem for setup, administration, troubleshooting and tuning

How you'll stand out

  • Experience with Python

  • Some knowledge of Java, nice to have

  • Some knowledge in ML Ops

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Dataiku is a massive, highly disruptive enterprise AI and machine learning platform fundamentally designed to democratize data science and bring "Everyday AI" to global organizations. Founded in 2013 by Florian Douetteau, Clément Stenac, Marc Batty, and Thomas Cabrol in Paris (and now headquartered in New York City), the company operates as the ultimate collaborative digital operating system for data teams. Under the hood, Dataiku seamlessly bridges the gap between advanced data scientists, data engineers, and business analysts—providing a unified, end-to-end environment for data preparation, predictive modeling, MLOps, and cutting-edge Generative AI integration. Their primary target audience spans massive Fortune 500 enterprises (including industry heavyweights like Unilever, GE, and BNP Paribas) who desperately need to break down data silos, scale their AI initiatives, and transition from clunky legacy analytics to fully automated, predictive data pipelines. What sets Dataiku apart in the fiercely competitive AI landscape is its uncompromising commitment to visual, low-code/no-code collaboration; completely empowering non-technical domain experts to build robust machine learning models while still offering deep, code-first flexibility for elite engineering teams.

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