5 meilleures offres (python developer, switzerland) (à pourvoir)

Data Scientist, Product Analytics
SonarSource —Genève, GE
- 100%
- SQL
- Esprit d'analyse
- Technologies de l'information
2 h
Head of Algorithmic Trading Governance
Alpiq Holding AG —Lausanne, VD
- 100%
- Gestion du personnel
- Leadership
- Compétences en communication
7 h
Infrastructure Engineer
Laelaps AI —Zürich, ZH
- 100%
- Niveau Doctorat
- AWS
- Python
Postuler rapidement
il y a 2 jours
Enterprise Solutions Engineer - Switzerland
ElevenLabs —Suisse
- 100%
- Télétravail
- Anglais
- Développement logiciel
- Technologies de l'information
Senior FullStack Developer
Luxoft —Zürich, ZH
- Anglais
- Java
- Python
2 h

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Data Scientist, Product Analytics

SonarSource -
Genève, GE

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Détails de l'emploi

100% | Temps de travail : 90-100%
Il y a 2 heures

Avantages

Logement

Qualifications

SQL
Esprit d'analyse
GitHub
Technologies de l'information
Compétences en communication
Python

Description complète du poste

Who is Sonar?

Sonar is driving the future of agent-centric software development. As the leader in AI code verification and governance, we solve a critical problem: ensuring that software generated by AI-assisted developers or autonomous agents is reliable, secure, and maintainable.

Integrating seamlessly with Claude Code, Codex, Cursor, GitHub Copilot, Gemini, and Devin, we help over 75% of the Fortune 100 build trusted, reliable, compliant software. Customers who use Sonar are 44% less likely to report an outage due to AI-generated code.

We believe code verification is the critical missing link in the Agent-Centric Development Cycle (AC/DC). Industry giants like Nvidia, ServiceNow, Booking.com, Goldman Sachs, AstraZeneca, and Ford Motor Company count on us to provide independent, explainable, consistent review and governance of their AI-generated code via products like:

SonarQube: The world’s leading AI code review and verification platform.
SonarQube Foundation Agent: Currently topping the leaderboards for agentic software repair.
SonarSweep & Sonar Context Augmentation: Providing the enterprise-grade context and constraints agents need to be truly effective.

Our team operates across global hubs in Austin, Bochum, Dubai, Geneva, London, Singapore, Tokyo, and Washington D.C. We move with a mindset we call CODE:

Committed to our customers and community.
Obsessed with quality.
Deliberate in our decisions.
Effective as one team.

With over $400M in revenue and profitable, fast-paced growth, we are building the backbone of the AI software revolution. If you’re hungry to have an impact, want to build at a fast pace, and ready to work at the forefront of AI, we want to hear from you.

Position description

Operating within the Data & Insights team, your mission is to decode customer behaviors and transform product engagement into high-impact, actionable intelligence. By bridging the gap between product adoption and commercial data, you will construct a holistic view of user health and the tangible value we provide.

You will take full ownership of the product-usage analytics lifecycle. This involves hands-on data exploration, hypothesis testing, and deep collaboration with Data and Analytics Engineers to architect the foundational models you require. Success is defined by analytical rigor and your ability to steer strategic product and Go-to-Market (GTM) decisions.

Embedded in our central Data & Insights department, this role serves as a strategic partner to our Product organisation, driving cross-functional synergy with other departments, engineering, and data science teams to maximize overall success.

Success depends on genuinely understanding the product you're analyzing, not just the data behind it.

What you will do

Understand product usage. Work closely with product managers (PMs), building a deep understanding of the product itself, to analyze how customers adopt and use it, and identify patterns in engagement, feature usage, and retention.to Analyze how customers adopt and use the product, and identify patterns in engagement, feature usage, and retention.
Connect usage to outcomes. Link product usage data to customer and sales data to reveal how usage relates to expansion, churn risk, and account health.
Define usage-based signals. Build the logic behind usage scoring, health indicators, and other metrics that translate behavior into insights that drive activation, retention, and expansion.
Be proactive. Explore product, sales, and customer data to surface findings nobody asked for. Anticipate the next question.
Run experiments and validate. Test which usage signals actually predict outcomes, applying sound statistical methods and being honest about significance and causality.
Partner on the data foundation. Work with Data and Analytics Engineers to get product and sales data into the warehouse and modeled well. Define requirements and contribute to data models that connect product usage with customer and sales data.
Tell the story. Communicate insights clearly to stakeholders across the business, and document context, caveats, and decisions so the work survives handoffs.

Experience and qualifications

Deep product understanding and a track record of working effectively with PMs, translating product knowledge and usage data into decisions they act on.
Strong analytical track record: someone who has measurably influenced revenue, product, or GTM decisions through analysis, not just produced reports.
Comfort with the latest AI tools, and a habit of using them to work faster and sharper: You stay current as the tooling evolves and bring new approaches to the team.
Eagerness to develop: you actively grow your skills, seek feedback, and treat new tools and methods as opportunities rather than threats.
Solid SQL. You can independently query, join, and explore data without waiting for someone to prepare it for you.
Proficiency in Python for analysis, modeling, and automation. Experience with ML and statistical libraries (e.g. scikit-learn, statsmodels) for modeling, prediction, and inference.
Statistical foundation: experimentation, significance testing, regression, segmentation, forecasting, and the judgment to know which applies.
Working knowledge of how product usage connects to revenue: funnels, cohorts, retention, account health, and the realities of joining product usage data with CRM and sales data.
Willingness to get hands-on with data modeling. You don't need to be a dbt expert, but you must be comfortable exploring messy data and partnering on (or building) the models you need rather than waiting for clean tables.
Strong communication and stakeholder skills: you can challenge weak measurement respectfully and make a recommendation, not just present options.
Proactivity and autonomy: you raise your hand early, plan your own work, and look for impact without being asked.

In-office culture

We're intentional about this. We believe the best teams are built in the room together. Three anchor days — Mondays, Tuesdays, and Thursdays — create the collaboration rhythm that makes a hub office worth having.

Candidates need to be genuinely based in the location the role is posted — if that's not where you are today, we're happy to support relocation for the right person.

We value diversity, equity, and inclusion

At Sonar, we believe that our diversity is our strength. We are a global company that values and respects different backgrounds, perspectives, and cultures. We are committed to fostering a diverse and inclusive work environment where everyone feels valued and empowered to contribute their best. We are proud to be an equal opportunity employer and welcome all qualified applicants, regardless of race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

If you need any accommodation, please reach out to us at [email protected].

All offers of employment at Sonar are contingent upon the results of a comprehensive background check and reference verification conducted before the start date.

Applications that are submitted through agencies or third party recruiters will not be considered.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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Data Scientist, Product Analytics

Head of Algorithmic Trading Governance

Infrastructure Engineer

Enterprise Solutions Engineer - Switzerland

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