Data Scientist

Title: Data Scientist (L-6)

Reports To: Chief Executive Officer

Location: Remote

Position Type: Full Time, Exempt

Compensation: $150,000 - $250,000 OTE, which includes base salary and on-target bonus

Leverage is seeking a hands-on Data Scientist to serve as the principal data science resource at our profitable startup company. This is a player/coach role where you spend much of your time doing the work: running experiments, building and refining models, honing our dataset, and improving targeting accuracy. But you will also work closely with executive leadership to shape the research roadmap and translate business goals into analytical priorities.

The right candidate is equally comfortable riffing on new ideas with leadership and spending long stretches heads-down in Python. You will work in a small, scrappy environment — but we are looking for someone whose background reflects rigor, excellence, and exposure to high standards of data science practice.

Your work will directly and measurably impact client outcomes: improving campaign conversion rates and reducing costs for the organizations we serve. If that kind of tangible, mission-driven impact excites you, this role was built for you.

JOB DESCRIPTION:

l Take ownership of our existing modeling work, understanding the current methodology and identifying opportunities to improve accuracy, efficiency, and interpretability.

l Work closely with executive leadership to develop and prioritize a research roadmap — translating high-level business goals into specific experiments, hypotheses, and analytical initiatives.

l Conduct exploratory data analysis to identify meaningful signals — beyond surface-level demographic trends — that correlate with donor likelihood, engagement, and conversion.

l Use feature selection, weighting, modeling approaches, validation, and performance evaluation to improve donation propensity prediction. Pursue both quick wins and longer-term modeling improvements in parallel.

l Design and execute A/B experiments — setting up hypotheses, computing statistical significance, establishing baselines, and translating results into business recommendations.

l Review and make recommendations on how first-party, third-party, and enriched datasets should be structured, grouped, and organized for effective modeling and scoring.

l Explore, test, and recommend modern machine learning or AI techniques that may improve predictive accuracy, interpretability, or automation — while knowing when simpler approaches are more appropriate.

l Use available data to identify potential audience personas and behavioral patterns that can inform targeting strategy and client recommendations.

l Implement MLOps best practices: model versioning, performance monitoring, drift detection, and retraining pipelines to keep models’ production-ready and sustainable.

l Ensure responsible data handling across all modeling work, maintaining awareness of applicable privacy guidelines and political data compliance requirements.

l Clearly document assumptions, methodology, code, and results to ensure continuity and institutional knowledge.

REQUIREMENTS:

l 5–7+ years of professional experience in data science, statistical modeling, applied machine learning, or quantitative research.

l Strong academic background (MS or PhD preferred) in a quantitative field, or equivalent experience at a quick-paced technology company, research institution, or data-driven organization.

l Advanced proficiency in Python, SQL, and common data science libraries (scikit-learn, pandas, numpy, XGBoost, etc.).

l Hands-on experience on AWS or other cloud platforms including data storage (S3, BigQuery, Redshift) and ML services (SageMaker or equivalent).

l Proficiency with data visualization tools (Tableau, Looker, Plotly, or similar) for communicating findings to non-technical leadership.

l Skilled at structuring ambiguous questions into testable hypotheses — comfortable operating in greenfield environments where the roadmap is shaped collaboratively, not handed down.

l Experience working with large, messy, multi-source datasets — including joining, cleaning, feature engineering, and exploratory analysis.

l Strong understanding of model evaluation practices including lift, precision/recall, cross-validation, baseline comparisons, and out-of-sample testing.

l Familiarity with MLOps practices: model monitoring, drift detection, experiment tracking (MLflow, Weights & Biases, or similar), and production deployment pipelines.

l Able to separate interesting correlations from genuinely actionable predictors.

l Communicates clearly with both technical and non-technical stakeholders — able to translate complex findings into practical business implications without losing rigor.

l Comfortable being the principal data science voice at a small company — able to own a research agenda, manage your own priorities, and deliver outcomes with limited oversight.

l Experience in donor modeling, fundraising data, political data, adtech, martech, or audience targeting is a plus — but intellectual curiosity about these domains matters more than direct experience.

l Pragmatic, curious, collaborative, and grounded. You are energized by solving the right problem via the most efficient path.

l Experience with NLP or text-based feature extraction from unstructured data (survey responses, social signals, petition text).

IS THIS ROLE FOR YOU?

You should apply if you:

l Want to own a data science function end-to-end at a company where your work has direct, measurable impact on real-world outcomes

l Are energized by a mix of exploratory research and hands-on execution. You are just as happy running an experiment as you are presenting findings

l Thrive in small, high-trust environments where you have real autonomy and direct access to leadership

l Care about the missions of the organizations we serve — progressive campaigns, nonprofits, and social-good organizations

l Want to build something from the ground up rather than maintain what already exists

You might not be the right fit if you:

l Prefer a clearly defined scope with little ambiguity

l Want a large team of engineers and analysts supporting your work

l Are primarily interested in research for its own sake rather than research that drives measurable business outcomes

* l Do not feel motivated to support the success of left-of-center campaigns and social-good nonprofits