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Machine Learning Manager 🇪🇺

Are you looking to have an impact on the daily life of millions of entrepreneurs in France (and tomorrow in Europe) ?

Are you looking for a work environment that values trust, proactivity, and autonomy?

Then Pennylane is the right place for you !


Our vision

We aim to become the most beloved financial Operating System of French SMEs (and soon, European ones).

We help entrepreneurs rid themselves of time-consuming tasks related to accounting and finance while providing them with access to key financial information to assist in making the best decisions for their business.


About us

Pennylane is one of the fastest growing Fintechs in France (and soon to be in Europe!)

In 5 years of existence, we’ve managed to :

💻 Make ourselves known as a groundbreaking accounting and financial software for small businesses and their accountants. 💰 Raise a total of €150 millions, including from Sequoia, the famous fund from the Silicon Valley who invested early in companies like Google, Facebook, Airbnb, Stripe, Paypal and much more...

👨‍👩‍👧‍👦 Grow from 7 cofounders to 500+ happy Pennylaners : we’re now recognized as one of the greatest places to work in France (and also remotely), with a 4.6/5 rating on Glassdoor.

🌍 Build an international environment with more than 25 nationalities, with a strong remote-friendly culture, where 30% of the employees are already working from all parts of Europe.

🤝 Earn the trust of thousands of customers and accounting firms and obtain outstanding ratings.

🚀 Already more than 200,000 small and medium-sized enterprises (SMEs) and over 3,500 accounting firms use Pennylane in France!


WHY this position is of utmost importance to reach our mission

At Pennylane, every decision that is not backed by data is likely to be challenged (when data is available of course). This culture is embraced by the leadership team, and actively promoted everywhere. The role of the data team is to make this possible by treating data like a production asset, making it available company-wide, and using it proactively to improve Pennylane’s user experience.

By joining us as a Machine Learning Manager, you will have a pivotal role in large projects, bringing your engineering and machine learning expertise to help us meet our high delivery standards.


HOW you will contribute to the company

- As a Machine Learning Manager, you will be part of the Machine Learning team (5 people), inside the Data department (20+ people).

- You will contribute technically to the design and implementation of machine learning solutions and tools across the entire ML lifecycle, from model training and tuning to deployment, inference, experimentation and monitoring.

- You will collaborate with Product Managers to ensure the highest impact and quality of machine learning work for our users, as well as the best atmosphere and motivation in the team.

- You will grow your team continuously, and team up with other managers to set up the right culture and processes to enable people.

- You will work closely with data engineers and software engineers to quickly deploy end-to-end solutions with a direct impact on our users, and improve our machine learning ecosystem.


WHAT you can expect from your life at Pennylane

Within one month:

- You will learn everything about our company, our teams, and our vision during the first onboarding week.

- You will get to know your team and start taking over people and technical topics.

- You will get familiar with our stack, and have delivered a few small projects which will give you a concrete taste of our tools & processes.

- You will be given time to meet your future stakeholders, and gain a deep knowledge of our product and operations.


Within 3 months:

- You will be fully in charge of your team.

- You will be accountable for items in our roadmap, defining and prioritizing team work autonomously.

- You will be comfortable with our technical stack (AWS, Terraform, streaming, batch, scheduling, warehousing).


Within 6 months:

- You will be involved in cross-team management initiatives.

- You will collaborate closely with Product and Tech teams to steer the team’s roadmap.

- You will work with engineers and ML practitioners on improving our platform.


And beyond: the data team will continue growing with the company

Which means:

- Opportunities to recruit, mentor and manage new team members,

- Increased accountability in project leadership,

- Responsibilities to design and implement new processes, tools and best practices to make sure that your team works even more efficiently.


 Who are we looking for?

You’re the right candidate if you:


- Have had previous experiences in Machine Learning Engineering, ideally in scale-up environments (4-5 years)

- Have at least 2 years of experience managing Machine Learning profiles

- Are seasoned working with Python and cloud technologies (ideally AWS)

- Are looking for a hands-on management role (around 50%)

- Are confortable collaborating with cross-functional stakeholders beyond the data team

- Like being challenged and solving complex problems (accounting is complex in Europe)

- Have strong communication skills and are capable of articulating your ideas clearly

- Are Fluent in English - French is a plus


What does the recruitment process look like?

- A first interview with our Talent Acquisition Manager (20-30 min)

- A case study interview to discuss a topic closely related to one of our priorities (75 min)

- A past project interview to hear about your experience (60 min)

- A last interview with our Head of Data and our CTO to discuss your management experience and our company culture (60 min)


We make sure we move fast; you can expect the recruitment process with us to last between 15 and 25 days in total.


What do we do to make your work life easier?

🏥 You will have a great healthcare cover (Alan Blue) to take care of yourself and your family

🍜 You will have lunch credits (Swile card) to buy your favorite food every day

🏢 You will be able to work from our wonderful office in the center of Paris, or from any WeWork in Europe

🏡 If you have a fully remote contract, you will have a budget to turn your home into a more comfortable workspace, as well as a monthly allowance to work from a coworking space whenever you feel like it

🏝You will get 10 additional days off (to the 25 standard ones) to rest and do what you love each year

Through our partner Gymlib, you will have access to 8000 fitness spaces and more than 300 activities related to wellness

🇬🇧 You will have access to Busuu to perfect your english or learn a new langage of your choice

💻 You will get the latest Apple equipment

🎉 You will be part of a vibrant social community: we do lots of sports together (foot, running, climbing...), we love to hang out and have a drink together (Thursday afterwork drinks on our rooftop is a usual thing), twice-a-year we hold company seminars (last time we went on a trip to the French Alps and it was fabulous!)


Closing words

We have tried to give you as much details as we could about the purpose, missions and environment of this role; it is now up to you to tell us if you think this is a position in which you could a) have fun and b) bring value to our company.


If you’re hesitating, we encourage you to apply anyway: worst case scenario, you might lose a few minutes, and in the best case, it will be the start of a meaningful and long-lasting collaboration.


We also want to emphasize that we fully embrace diversity and that we’re doing our best to create a safe and inclusive environment. We are committed to providing an equal employment opportunity regardless of gender, sexual orientation, origin, disabilities, or any other traits that make you who you are. If anything, diversity makes us a more fun place to work at.


To end with a humble illustration of our commitment, we’ve recently signed the Pacte Parité to set concrete goals in building a better and more inclusive workplace in the years to come.


Average salary estimate

$82500 / YEARLY (est.)
min
max
$70000K
$95000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Machine Learning Manager 🇪🇺, Pennylane

Are you an innovative leader with a passion for machine learning? Pennylane, one of France's fastest-growing Fintechs, is on the lookout for a Machine Learning Manager to join our dynamic team. With a vision to revolutionize financial operations for SMEs across Europe, we're committed to streamlining cumbersome accounting tasks while delivering impactful financial insights to entrepreneurs. As the Machine Learning Manager, you will play a critical role in shaping our approach to data, managing a talented team of five machine learning professionals within a larger 20+ person data department. You'll dive into the entire ML lifecycle—from model training to deployment—while ensuring collaboration with Product Managers to achieve high-quality outcomes. Your leadership will cultivate a culture of innovation, and you’ll work closely with engineers to deploy efficient, insightful solutions directly impacting our users' experience. Join us and be part of a vibrant environment, where over 500 team members bring diverse perspectives together, committed to making a difference in the world of finance. If you're excited about tackling complex problems and leading a proactive team, Pennylane is the place for you!

Frequently Asked Questions (FAQs) for Machine Learning Manager 🇪🇺 Role at Pennylane
What are the main responsibilities of a Machine Learning Manager at Pennylane?

As a Machine Learning Manager at Pennylane, you will oversee your team's operations, ensuring the delivery of efficient machine learning solutions. Your responsibilities include leading projects throughout the ML lifecycle, collaborating with Product Managers, and promoting a productive team culture. You'll also mentor your team members and coordinate with other departments to enhance our machine learning capabilities.

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What qualifications are necessary for the Machine Learning Manager role at Pennylane?

Pennylane seeks candidates who have extensive experience in Machine Learning Engineering—typically around 4-5 years in similar roles, plus at least 2 years of management experience. Proficiency in Python and cloud technologies, especially AWS, is essential. The ideal candidate should also have experience in collaborative environments and possess strong communication skills.

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How does the recruitment process for the Machine Learning Manager position at Pennylane work?

The recruitment process for the Machine Learning Manager position at Pennylane includes several stages: a preliminary interview with our Talent Acquisition Manager, a case study discussion, a review of your past projects, and a final interview with senior management. Overall, the process typically lasts between 15 and 25 days, allowing for a smooth and swift selection.

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What kind of work environment can a Machine Learning Manager expect at Pennylane?

At Pennylane, the work environment is vibrant and collaborative, with a focus on innovation and flexibility. You will be part of a diverse team of over 500 professionals, with a strong emphasis on trust, autonomy, and a remote-friendly culture. Regular team-building activities, professional development opportunities, and a robust support structure ensure that every team member has the chance to thrive.

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Are there any particular challenges a Machine Learning Manager at Pennylane might face?

A Machine Learning Manager at Pennylane may encounter the complexities of implementing machine learning solutions within the unique context of accounting and finance, especially given the intricacies of the European financial landscape. Navigating these challenges while delivering high-quality, impactful solutions is both a challenge and a significant part of the role.

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Common Interview Questions for Machine Learning Manager 🇪🇺
Can you describe your experience with machine learning algorithms?

When answering this question, highlight specific algorithms you’ve worked with and the contexts in which you applied them. Discuss any projects where you successfully implemented these algorithms to solve problems or enhance processes.

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How do you prioritize machine learning projects within your team?

Discuss your approach to balancing short-term and long-term priorities, stakeholder needs, and team capacity. Emphasize your ability to utilize data insights and past performance metrics to inform your prioritization.

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What is your management style when leading a machine learning team?

Explain your approach to leadership, whether it’s hands-on or more delegative, and how you adapt your style depending on project needs and team dynamics. Highlight your focus on fostering a trusting and collaborative team environment.

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Can you provide an example of a complex problem you solved with machine learning?

Detail a specific challenge you faced, the methodology you employed to address it, and the outcome. Highlight how your solution improved processes or provided new insights, illustrating both technical and managerial skills.

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How do you ensure the quality and reliability of your machine learning models?

Discuss best practices for model evaluation and validation you regularly utilize, such as cross-validation, A/B testing, and regular monitoring. This shows your commitment to maintaining high standards in your machine learning outputs.

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What strategies do you use to keep abreast of advancements in machine learning?

Mention resources like relevant publications, online courses, or networking within the ML community. This highlights your dedication to continuous learning and keeping your skills relevant.

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How do you handle conflicts within your team?

Illustrate your conflict resolution skills with examples of how you’ve successfully mediated disputes in the past, fostering an environment of open communication and trust.

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What is your experience with deploying machine learning models in production?

Describe your approach to deploying models, including the technologies used, the steps taken to ensure successful implementation, and any challenges you've faced along the way.

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How do you collaborate with cross-functional teams?

Explain how you build relationships with teams outside of your own, focusing on how effective communication and shared goals drive successful collaborations on machine learning initiatives.

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What are the key metrics you use to measure the success of machine learning projects?

Identify specific metrics, such as accuracy, precision, recall, or end-user satisfaction that you utilize to gauge project success. This illustrates your analytical mindset and attention to results.

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Full-time, hybrid
DATE POSTED
January 10, 2025

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