Senior Data Scientist

Team Description 

The Machine Learning Group at Pendo is dedicated to enhancing Pendo’s platform by delivering actionable insights and reliable predictions that our customers trust and value. Our team is highly committed to adopting good research and development practices while maintaining a keen understanding of the customer's perspective, ensuring that our solutions are technically robust and aligned with their needs.

Our solutions include diverse focus areas, from classical ML algorithms to cutting-edge generative models embedded in Pendo’s core product suite. By combining qualitative and quantitative data, we take projects from initial ideation to production, aiming to provide better and actionable insights to our customers.

As a team member, you'll collaborate closely with talented individuals, bringing together their diverse skill sets to solve complex problems and drive innovation. Our team culture is built on mutual respect, trust, and a genuine passion for our work. By joining our team, you'll contribute to developing state-of-the-art solutions that power Pendo and play a crucial role in shaping the company's future. At the Machine Learning Group, we emphasize valuing your expertise, nurturing your personal growth, and ensuring your impact is felt across the entire organization.

Role Responsibilities 

• Be an integral part of delivering end-to-end ML solutions, from initial ideation to production - an all-around Data Scientist.
• Define the project scope and set success criteria together with the Product team.
• Explore data, formulate and present PoCs (Proof of Concepts).
• Develop, measure, and deploy models to production as well as monitor them and understand the customer experience and the gaps
• Continuously explore Machine and Deep Learning algorithms that can be harnessed for our mission to improve customer experience.
• Contribute to our ML infrastructure by designing and implementing scalable machine learning pipelines together with the team’s Machine Learning Engineers.

Minimum Qualifications 

• M.Sc. or Ph.D. degree in Computer Science or equivalent.
• At least 5 years of hands-on experience in developing and delivering ML components to production at scale.
• Production-grade Python coding experience.
• Ability to break a business problem into ML components.
• Team player and able to collaborate well with others.

Preferred Qualifications 

• Experienced with cloud technologies (GCP/AWS/Azure).
• Experience deploying AI-based solutions to production (3rd Party / self-hosted LLMs).

Pendo Description:

Pendo was founded in 2013 by former product managers, who combined their heads and hearts to build something they wanted but never had as product managers -- a simple way to understand and attack what truly drives product success.  Our mission is to improve society's experience with software.

Come join one of the fastest-growing startups, supported by best-in-class institutions like Battery Ventures, Salesforce Ventures, Spark Capital and Meritech. You will gain experience in a diverse and exciting set of technologies and clients and have a real impact on Pendo's future. Our culture is passionate, dynamic, and fun.

EEOC

We are an equal opportunity employer and believe having diverse teams where everyone brings their whole self to Pendo is key to our success. We welcome all people of different backgrounds, experiences, abilities and perspectives.

Accessibility

Pendo is committed to working with, and providing access and reasonable accommodation to, applicants with mental and/or physical disabilities. If you think you may require an accommodation for any part of the recruitment process, please send a request to: accommodation@pendo.io. All requests for accommodations are treated discreetly and confidentially, as practical and permitted by law.

Compensation

Our salary ranges are based on paying competitively for our size and industry, and are one part of many compensation, benefits and other reward opportunities we provide.

Individual pay rate decisions, including offers made within and over the expected salary range, are based on a number of factors, including qualifications for the role, experience level, skillset, and balancing internal equity relative to peers at the company.