Staff Data Scientist

Engineering at Crunchbase
The Crunchbase engineering team is a dynamic, fast paced team that is committed to quickly delivering features, evaluating their performance, and iterating towards the product vision shared by the company.
We are organized in vertical teams that include cross-functional engineers as well as functional guilds aimed at working tightly with the product team to deliver high standards code while iterating quickly on features. 
We value open and honest communication, and strive to create an environment where opinions and views can be shared and considered in an effort to reach the best decision.

Signals Team at Crunchbase
The Crunchbase Signals Team thrives to provide users with insightful information throughout their experience on Crunchbase platform. By combining Crunchbase, private and behavioral data with machine learning systems, we aim to understand the underlying data needs to best assist their day to day work, we then identify and highlight relevant insights and predictions, eventually build personalized experiences that are uniquely valuable to our users. 

The team is composed of Data Scientists, Machine Learning Engineers and Data Engineers working together to deliver models and services into production. We are hiring a hands-on Staff Data Scientist to help shape and build out the intelligence layer for Crunchbase signals products. We are looking to go deeper on the data we have, generating and presenting insights and predictions that help our users better understand our data and offer observations and recommendations specifically tailored to their use-case. The ideal candidate is a creative innovator and an outstanding communicator with strong hands-on skills that love to mentor and work with a team. They are not only curious about data and what it can teach us, but also care deeply about our users and how to drive an experience that helps them engage with and better utilize our data. 

Essential Duties/Responsibilities:

• Dimensional Data Modeling: Architect and design new dimensional data models aligning with business objectives, ensuring efficient data accessibility, and optimize schema designs for scalability, flexibility, and adherence to industry best practices.
• ETL Pipeline Development and Maintenance: Build, monitor, and maintain analytics and production data ETL pipelines, ensuring the reliability and efficiency of data workflows.
• Data-Driven Culture Empowerment: Provide the foundation for a data-driven culture by developing tools and processes for engineers and the Product team to easily query and analyze datasets.
• Scale ML Algorithms: Enable data scientists to implement Natural Language Processing (NLP) and Machine Learning (ML) algorithms at scale while designing fault-tolerant, highly available systems to support the deployment and execution of ML models.
• Technical Leadership: Contribute to the design key component in our architecture and platform. Consider business requirements and tradeoffs and implement a variety of solutions to address our data needs of our data architecture, ensuring scalability and efficiency. Own key projects end-to-end and influence the mid and long term technical direction of our data platform.
• Mentorship: Mentor junior engineers, foster a culture of technical excellence,  professional growth and continuous learning.
• Cross-Functional Collaboration: Collaborate with other engineering teams, product and analytics engineers, data scientists, and other team members to integrate solutions into our datatech ecosystem. Communicate effectively with non-technical stakeholders to gather requirements and provide data engineering support.
• Technology Evaluation and Adoption: Stay informed about emerging technologies and industry best practices. Evaluate and recommend new tools and technologies to enhance our data capabilities.
• Communication: Communicate complex technical concepts and findings to non-technical stakeholders in a clear and concise manner.
• Performs other related duties as assigned.

Supervisory Responsibilities:

• None.

Required Skills/Abilities:

• Excellent communication skills, capable of simplifying complex issues for effective collaboration.
• Proven track record in dimensional data modeling, ETL pipeline development, and system architecture.
• Proficiency in Python
• Familiarity with data warehousing technologies and cloud platforms.
• Experience implementing NLP and ML algorithms in production environments.
• Strong problem-solving skills and effective communication abilities.
• Demonstrated leadership skills with the ability to guide and mentor team members.
• Proficient with Google Office Suite or related software.

Education and Experience:

• Bachelor's degree in Computer Science, Engineering, or a related field preferred.
• 6+ years of professional engineering experience working with technologies (Python, SQL, pandas, etc).

Physical Requirements:

• Prolonged periods of sitting at a desk and working on a computer.