Data Scientist

About G2 - Our People 

G2 was founded to create a place where people will love to work. We strive to create meaning in work and provide more than just a job: a true calling. At the heart of our community and culture are our people. Our global G2 team comes from a wide range of backgrounds and experiences, and that’s what makes our G2 community  strong and vibrant. We want everyone to bring their authentic selves to work, and we do this through our company and team events, our G2 Gives charitable initiatives, and our Employee Resource Groups (ERGs). 

Our employee-led, leadership-supported ERGs celebrate the diversity of our team, foster inclusivity and belonging, and create a space to connect to each other. Through connections and understanding, we build a stronger and more dynamic global team and help every person reach their personal peak.

We support our employees by offering generous benefits, such as flexible work, ample parental leave, and unlimited PTO. Click here to learn more about our benefits. 

 

About G2 - The Company

When you join G2, you join the global team behind the largest and most trusted software marketplace. Every month, 5.5 million people come to G2 to inform smarter software decisions based on honest peer reviews. Authenticity is our focus, and every day we help thousands of companies, and hundreds of employees, propel their potential. Ready for meaningful work that starts and ends with compassion and heart? You’ve come to the right place.

G2 is going through exciting growth! We’ve recently secured our Series D funding of $157 million, which will further allow us to grow and develop our product and people. Read about it here!

About The Role

As a Data Scientist, you will focus on developing machine learning models to solve complex business problems. In addition to core data science responsibilities, you will occasionally step into ML engineering tasks, such as troubleshooting pipelines and ensuring operational stability. This role is ideal for a data scientist with strong technical skills and an interest in supporting model deployment and production workflows when needed. You'll also mentor junior team members and foster collaboration across teams, contributing to a culture of shared knowledge and continuous improvement

In This Role, You Will:

• Lead the development and refinement of machine learning models, including feature engineering, algorithm selection, and model optimization.
• Conduct experiments with advanced machine learning techniques to improve model performance and deliver impactful solutions.
• Build, maintain, and optimize data pipelines to support end-to-end machine learning workflows.
• Analyze large datasets to extract insights and provide actionable recommendations for business teams in conjunction with model development
• Collaborate with ML engineers to operationalize models, ensuring scalability and reliability.
• Work closely with cross-functional teams, including product managers and engineers, to translate business requirements into machine learning solutions.
• Document and present methodologies, findings, and results to both technical and non-technical audiences.
• Act as an on-call resource to troubleshoot and resolve issues with deployed machine learning models.
• Collaborate with ML engineers to monitor model performance and ensure operational stability.
• Mentor junior team members, providing technical support, guidance on model development, and best practices implementation.

Minimum Qualifications:

We realize applying for jobs can feel daunting at times. Even if you don’t check all the boxes in the job description, we encourage you to apply anyway. 

• 4+ years experience as a data scientist involved in data extraction, analysis and modeling.
• 4+ years of experience in Python and SQL
• Strong understanding of statistics
• Proficiency in machine learning algorithms and all stages of machine learning.
• Familiarity with neural networks and deep learning.
• Familiarity with AWS services and Snowflake (or similar SQL DB)
• Familiar with containerization (e.g., Docker) and API frameworks (e.g., Flask).
• Demonstrated ability to troubleshoot issues in production environments, including debugging data pipelines or model related errors.

What Can Help Your Application Stand Out:

• Successful end-to-end delivery of data s