Skyward
Program Delivery
SeniorDataScientist
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“Senior Data Scientist at Skyward. Skills: Data Science, Graph Analytics, Entity Resolution, Machine Learning. Lead end-to-end data science experiments. Run exploratory data analyses”
What You'll Achieve.
Trust, Truth, and Transparency continue to represent our brand; Reproducibility is a hard acceptance criterion; zero appetite for dashboard theater
Industry & Context.
solutions; risk modeling; entity resolution; anomaly detection; record linkage; finds the bug
Active Secret clearance, government-controlled environment, government contractor, Public Trust security clearance
What They're Looking For.
Must Have
7+ years of applied data science experience, at least 2 years that included graph analytics or network analysis, production-grade Python, pandas, NumPy, scikit-learn, networkx, graph-tool, igraph, at least one GNN library, PyTorch Geometric, DGL, Real-world experience with probabilistic record linkage / entity resolution, Senzing, dedupe, FEBRL, Magellan, homegrown Fellegi-Sunter implementation, Comfort working without labels, Anomaly detection, positive-unlabeled learning, isolation forests, autoencoders, Interpretability discipline, SHAP, feature importance, partial dependence plots, LLM application experience, Entity and relationship extraction, Statistical maturity, power analysis, confidence interval, p-hacking is bad, Comfort presenting technical findings to non-technical operational stakeholders, Reproducibility hygiene, version control, parameterized pipelines, deterministic seeds, pinned dependencies, READMEs that work
Nice to Have
Published research, open-source contributions, conference talks in entity resolution, graph ML, applied causal inference, Knowledge of commercial data sources, LexisNexis, Transunion, Babel Street, track record of standing in front of federal subject matter experts, reputation among teammates as the person who finds the bug
What You'll Do.
Lead end-to-end data science experiments
Run exploratory data analyses
Apply graph analytics
Train interpretable classifiers
Run probabilistic entity resolution
Apply LLM-based Named Entity Recognition
How You'll Work.
Team & Collaboration
Comfort presenting technical findings to non-technical operational stakeholders; reputation among teammates as the person who finds the bug
Communication Scope
explain in plain English; presenting technical findings to non-technical operational stakeholders
Full Job Description
## Description We are Skyward. That is, a love for people, for improvement, for human advancement through information technology. We are a people-centered business with a desire to serve others. We are diverse and unified; creative and collaborative; a collection of complementary, not competing talents. And though on the surface we remain relaxed, beneath, a torrent of energy links us to our civic tech mission. We stand by our values, and we won’t compromise on any of them. Integrity: We’re conscientious, intentional, and empathetic. Our words and actions align. That’s our character. Please don’t ask us to play another part, we’re poor actors. Compassionate: If we may borrow a quote from Theodore Roosevelt: “No one cares how much you know until they know how much you care.” Because our team is thoughtful and supportive, caring deeply for each other, our clients, and our work, this comes naturally. Inquisitive: We remain students by failing openly and turning lessons into solutions. Unconventional: For us, life isn’t what happens outside of work. Work happens inside of life and our culture erases the line often dividing the two. Authentic: Made possible only because we embody the values listed above. We’re relaxed and fun yet intensely curious and driven. Team members are placed with thought, care, and precision to ensure that Trust, Truth, and Transparency continue to represent our brand. Because of that, we continue Onward, Upward, and Skyward. ## What you'll do Lead end-to-end data science experiments. From a data readiness assessment, through clustering and topological risk modeling, into unstructured-data enrichment and entity resolution. Run exploratory data analyses (EDAs) on government-furnished data inside a government-controlled environment: profile completeness, find the schema mismatches, flag the gaps, and document what the data can and cannot support before a single model gets trained. Apply graph analytics. Leiden community detection, be
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