Senior Data Scientist ML Forecasting

Our Story So Far:

Since our founding in 2019, Pigment is one of the fastest-growing, SaaS companies in the world today. Our product, a highly efficient and complex Enterprise Performance Management (EPM) platform is helping other scaling companies achieve their financial goals by quickly responding to dynamic factors in their respective markets including Tech, Retail, CPG & Financial Services. 

In less than 5 years, Pigment has grown to over 350 employees across offices in New York, Toronto, London & Paris and attracted over $248M in investment from some of the top Venture Capital firms globally. Co-CEO, Romain Niccoli, best sums up our mission that Pigment is “On a clear path to drive innovation and change the status quo in an industry desperately in need of it. Pigment has never been more critical and relevant than it is today.”  


Job Overview

Pigment is at the forefront of business planning software. Our mission is to revolutionize how businesses plan, analyze, and predict their strategies. With AI we are pioneering a new pathway in user experience and business modeling.

We invite you to join our mission to reshape how businesses strategize and gear up for success, integrating AI into the fabric of Pigment. We're on the lookout for a dedicated and experienced Data Scientist with a solid background in scaling models.

In the role of Data Scientist specializing in Time Series Forecasting at Pigment, your contribution will be critical in building advanced forecasting models to enable our clients to make well-informed strategic decisions.

You'll work in a high-speed environment, applying your deep knowledge in time series analysis to enhance our SaaS platform.

Responsibilities:

- Develop and implement advanced statistical and deep learning models for time series forecasting.
- Collaborate with cross-functional teams (engineers, product and design) to integrate forecasting models into our product through user-friendly product features.
- Provide insights and recommendations to improve existing machine learning frameworks and practices.
- Stay updated with the latest developments in machine learning, specifically in time series forecasting.

Qualifications:

- Master's or Ph.D. in Statistics, Mathematics, Computer Science, or a related field.
- Strong experience (5+ years) and solid understanding of time series forecasting models, libraries, and frameworks (e.g., ARIMA, Prophet, LSTM).
- Strong proficiency in programming language, in particular Python. C# is a plus.
- Experience with Google Cloud Platform (GCP), particularly in deploying and scaling machine learning solutions.
- We are developing a product designed for business users (not data scientists), having a product-focused mindset is essential.
Excellent analytical, problem-solving, and communication skills.

Folks from underrepresented minorities: science shows that you are less likely to apply if you feel that you don’t have all the necessary prerequisites. If this description matches where you are now or what you'd like to grow into in your next position, we encourage you to apply.


What you will get

- Competitive salary
- Equity
- The best health insurance with Alan Blue entirely free for you and your family 💙
- Trust and flexible working hours
- Brand new offices in the heart of Paris (Bourse)
- Remote-friendly environment

Pigment is an equal opportunity employer. We believe diversity is a strength and fosters innovation. We are committed to enabling everyone to feel included and valued at the workplace.  All qualified applicants will receive consideration for employment without regard to age, color, family, gender identity, marital status, national origin, physical or mental disability,  sex (including pregnancy), sexual orientation, social origin, or any other characteristic protected by applicable laws. We may process your personal data in accordance with our HR Data Protection Notice.

Grow your company with the best planning & reporting SaaS solution. Forecast the future of your business with scenarios in real-time through charts, simulations and continuous modeling.

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