Deltek is a recognized global standard for project-based businesses, delivering software and information solutions. They are seeking an Associate Data Science Specialist to help establish their first internal data science capability, focusing on building a roadmap for data science and modernizing their data infrastructure.
Conduct stakeholder interviews across business units (Sales, Customer Success, Product, Marketing) to identify existing predictive analytics efforts, pain points, and opportunities
Document data requirements for analytics use cases: what history is needed, training cadence, scoring frequency, user groups, success metrics
Map existing analytical workflows and identify where machine learning could augment or replace current approaches
Assess data readiness for common SaaS use cases: customer health scoring, churn prediction, upsell/cross-sell propensity, lead scoring
Translate business problems into data science requirements and technical constraints into business language
Perform exploratory data analysis to assess data quality and readiness for modeling
Conduct statistical analyses to validate business hypotheses and quantify relationships in customer data
Analyze customer behavior patterns across the SaaS lifecycle (onboarding, adoption, expansion, renewal)
Contribute to our Snowflake data platform design with data science use cases in mind
Document findings, insights, and recommendations in clear, accessible formats for diverse audiences
Create presentations and visualizations that make analytical findings accessible to technical and non-technical stakeholders
Explain data science concepts, potential use cases, and limitations to business leaders
Facilitate discussions between business stakeholders and technical teams to align on priorities and approach
Build organizational understanding of what data science can and cannot do in a B2B SaaS context
Present discovery findings and recommendations to leadership
Qualification
Required
Bachelor's or Master's degree in Data Science, Statistics, Analytics, Business, Computer Science, or related quantitative field
Solid understanding of machine learning concepts: supervised/unsupervised learning, model evaluation, feature engineering, training vs. scoring
