Senior ML Manager - Voice Teams

About the role

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Strategic product roadmaps for voice ASR capabilities are defined and driven in collaboration with product and engineering partners. Decisions on architecture and trade-offs for the voice perimeter are owned to ensure coherent technical delivery across French, German, and other supported languages.

People management across two teams of eight ML engineers and data scientists is led, including coaching, development, and retention of direct reports. Performance management, career development, and retention for direct reports are owned to drive high standards and fast execution.

Technical ownership of architecture and trade-offs for the voice perimeter ensures visibility and deep understanding of system design and constraints.

Challenge and guidance for teams on complex ML systems are provided to build the right solutions correctly. Strategy and delivery of team roadmaps aligned with organizational OKRs are navigated by making fast, informed decisions amid ambiguity. Identification of synergies across ASR teams ensures technical coherence across the ASR perimeter, and broader Applied AI initiatives are contributed to as a senior engineering leader.

Requirements

3+ years of people management experience leading ML or Data Science engineering teams is required.

Strong technical background in Machine Learning, with classical ML as a must, underpins the ability to operate in this role.

Ability to understand, challenge, and make architectural decisions on complex ML systems benchmarked against leading industry standards is necessary.

Deep understanding of system design, trade-offs, and technical risk management is required for success.

Experience thriving in a startup-like environment with fast decisions, ambiguity, and frequent scope changes is essential.

Versatile profile comfortable operating across multiple technical domains simultaneously is expected.

Fluent English is required; French is a plus for collaboration.

Nice to have

Hands-on experience with ASR, speech processing, or audio ML is valued.

Familiarity with LLMs and their integration into production ML systems is considered an advantage.

Experience managing multi-team organizations or acting as a manager of managers is preferred.

Practical notes

Work mode is hybrid with three days per week in the office at the Levallois Perret Paris office.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Voice technology powers healthcare interactions through transcription, dictation, and telephone assistance.

General ML engineering roles involve research, delivery, and cross-team coherence in fast-paced settings.

Continuous learning programs and internal mobility support professional development.

Remote flexibility is provided as part of the work arrangement.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.