Job summary
Job post source
This job is directly from Blackbaud
Job overview
The Principal Data Scientist at Blackbaud leads advanced analytics and machine learning initiatives to enhance Blackbaud products, collaborating across teams to deliver intelligent solutions.
Responsibilities and impact
The role involves collaborating with product managers, data scientists, and engineers to develop machine learning solutions, leading stakeholder discussions, designing and deploying ML pipelines, improving predictions, mentoring team members, and contributing to thought leadership.
Compensation and benefits
The starting base pay ranges from $117,200 to $157,500 with benefits including medical, dental, vision insurance, 401(k) with employer match, flexible PTO, parental leave, volunteer vacation, pet insurance, legal and identity protection, and tuition reimbursement.
Experience and skills
Candidates need 5+ years of quantitative analysis experience, expertise in supervised machine learning, proficiency in Python or R, and ability to influence business decisions; preferred skills include time series modeling, NLP, Databricks, mlflow, data visualization, Generative AI, and Big Data.
Career development
Blackbaud supports career development and hiring from any location, offering opportunities to engage in cross-team training, mentorship, and thought leadership activities.
Work environment and culture
Blackbaud has a remote-first, flexible work culture emphasizing diversity, inclusion, and community connection, with a strong stance against recruitment fraud.
Company information
Blackbaud is a company focused on data intelligence and AI technologies, providing advanced analytics solutions integrated into their products and maintaining a commitment to diversity and inclusion.
Team overview
The Principal Data Scientist works within the Data Intelligence Center of Excellence, collaborating closely with data science, engineering teams, and product managers.
Job location and travel
This is a remote-first position, supporting hiring and career development from the candidate's current location.
Application process
Applicants are directed to apply through Blackbaud's official careers page or LinkedIn, with a clear warning against recruitment fraud and unsolicited agency resumes.
Unique job features
The role offers unique opportunities to work with cutting-edge AI technologies, participate in thought leadership, and contribute to innovative machine learning solutions within a remote-first environment.
Company overview
Blackbaud is a leading provider of cloud-based software solutions and services designed specifically for non-profit organizations, educational institutions, healthcare facilities, and other social good entities. The company generates revenue through subscription fees for its software products, which include fundraising, financial management, CRM, and analytics tools. Founded in 1981, Blackbaud has a rich history of innovation and has grown through strategic acquisitions, enhancing its product offerings and expanding its global footprint. The company's mission is to empower and connect organizations to increase their impact and drive social good.
How to land this job
Position your resume to highlight your expertise in supervised machine learning, quantitative analysis, and proficiency in Python or R, as these are core to the Principal Data Scientist role at Blackbaud.
Emphasize your experience collaborating cross-functionally with product managers, engineers, and data scientists, as well as your ability to lead stakeholder discussions and mentor team members.
Apply through multiple channels including Blackbaud's official careers page and LinkedIn to maximize your application visibility and ensure you meet their recruitment protocols.
Connect on LinkedIn with current employees in Blackbaud's Data Intelligence Center of Excellence or related data science teams; use ice breakers like commenting on recent Blackbaud AI initiatives or asking about their experience with Blackbaud's machine learning platforms.
Optimize your resume for ATS by incorporating keywords from the job description such as 'machine learning pipelines,' 'data discovery,' 'automated processes,' 'Python,' 'R,' and 'quantitative analysis' to pass initial screenings effectively.
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