Senior Data Scientist (Data Analytics)

Location: Remote in the United States or Canada

Company

hirify.global is building decision intelligence capabilities that support business planning, customer success, and operational decision-making.

What you will do

• Design and develop multi-horizon time-series forecasting models for revenue, retention, churn, expansion, adoption, and operational demand.
• Build simulation and scenario-modeling frameworks that combine forecasts, business constraints, and causal effects to evaluate strategies under uncertainty.
• Apply causal inference, experimentation, and uplift modeling to measure customer and business interventions against baseline trends.
• Develop Bayesian and probabilistic models that provide decision ranges and quantify uncertainty.
• Manage the full model lifecycle, from problem framing and feature engineering through deployment, monitoring, tuning, and validation.
• Partner with Success, data engineering, and platform teams to integrate scalable models into enterprise planning and operational workflows.

Requirements

• 7+ years of experience in data science, decision science, applied machine learning, statistical modeling, or advanced analytics in production environments.
• Advanced Python skills with pandas, NumPy, scikit-learn, and relevant statistical modeling libraries.
• Deep SQL proficiency and experience with large, complex datasets on Snowflake or a comparable cloud data platform.
• Extensive experience building production-grade time-series forecasting systems, including multi-horizon forecasting and rigorous back-testing.
• Experience constructing simulation models, including Monte Carlo models, to project outcomes and evaluate interdependencies in complex business systems.
• Hands-on experience measuring incrementality and treatment effects through experimentation, causal inference, observational methods, or uplift modeling.
• Experience applying Bayesian or probabilistic methods to uncertainty estimation and decision-making.
• Strong understanding of statistics, probability, experimental design, model explainability, and responsible modeling practices.
• Ability to translate ambiguous business problems into structured mathematical or statistical models and explain methods, limitations, uncertainty, and recommendations to business and executive stakeholders.
• Experience integrating models into planning or operational workflows where they are used for business decisions.

Nice to have

• Experience in subscription, SaaS, telecommunications, customer success, or customer lifecycle analytics.
• Familiarity with NRR, retention, churn, expansion, product adoption, customer health, and value-realization metrics.
• Experience with customer segmentation, risk identification, intervention prioritization, or expansion planning.
• Experience with causal modeling tools such as EconML, DoWhy, CausalML, PyMC, statsmodels, or equivalent libraries.
• Familiarity with MLOps practices for deployment, reproducibility, monitoring, and governance.
• Experience integrating analytical outputs into enterprise planning, reporting, and operational decision workflows.
• Master’s degree or Ph.D. in Data Science, Statistics, Computer Science, Applied Mathematics, Operations Research, Engineering, or a related quantitative discipline; equivalent practical experience is considered.

Culture & Benefits

• Remote work is available in the United States and Canada.
• The role may be eligible for a bonus as part of total compensation.
• Base pay varies by geographic location, residence, job-related knowledge, skills, and experience.
• Benefits information is provided through the company’s benefits program.