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Closed listing

Calix North America

Senior Data Scientist, Data Analytics

Remote, United States

This opening has closed.

The original listing is kept below for reference.

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About employer

Job details

Experience: 7+ years of experience in data science, decision science, applied machine learning, statistical modeling, or advanced analytics in production environments.
Posting language: English
Posted: Nov 2, 2026

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Original job description

Archived listing. The details below describe a past opening.

Calix is building the next generation of decision intelligence capabilities across its business. The Analytics & Decision Science team is responsible for moving beyond reporting to develop predictive, causal, and simulation-driven solutions that directly influence how the company plans and operates.

We are seeking a hands-on Senior Data Scientist to build the forecasting, simulation, and causal inference capabilities needed to evaluate business strategies, quantify uncertainty, and improve leadership decision-making. A primary focus of this role will be supporting the Success business unit’s objectives, including improving customer retention, expansion, adoption, and value realization.

This role will translate complex, real-world business systems into production-grade models and decision-support tools that deliver measurable business and customer outcomes.

What You’ll Do

Design and develop robust time-series forecasting models for critical business metrics, including revenue, retention, churn, expansion, customer adoption, and operational demand.
Partner with Success leaders to identify the key drivers of customer outcomes and develop analytical capabilities that support the business unit’s strategic and operational objectives.
Build simulation and scenario-modeling frameworks that integrate forecasts, business constraints, and causal effects to evaluate strategies and support decisions under uncertainty.
Apply causal inference, experimentation, and uplift modeling to isolate and measure the impact of customer and business interventions against baseline trends.
Develop Bayesian and probabilistic models that quantify uncertainty and provide leaders with decision ranges rather than point estimates alone.
Use historical and current enterprise data to train, back-test, validate, and continuously improve models.
Manage the end-to-end model lifecycle, including problem framing, feature engineering, model development, deployment, monitoring, and tuning.
Partner with data engineering and platform teams to deploy scalable models that integrate with enterprise data systems and Success business workflows.
Establish rigorous evaluation practices, including back-testing, sensitivity analysis, explainability, and measurement of customer and business impact.
Translate complex customer and business dynamics into clear scenarios, insights, and recommendations aligned to leadership decision points.

What We’re Looking For

7+ years of experience in data science, decision science, applied machine learning, statistical modeling, or advanced analytics in production environments.
Advanced Python skills, including experience with pandas, NumPy, scikit-learn, and relevant statistical modeling libraries.
Deep SQL proficiency and experience working 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, such as Monte Carlo models, to project outcomes and evaluate interdependencies across complex business systems.
Hands-on experience measuring incrementality and treatment effects using 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.
Demonstrated ability to translate ambiguous business problems into structured mathematical or statistical models.
Experience integrating models into planning or operational workflows where they are actively used to make business decisions.
Strong communication skills, including the ability to explain methods, limitations, uncertainty, and recommendations to business and executive stakeholders.

Preferred Qualifications

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 developing models that support customer segmentation, risk identification, intervention prioritization, or expansion planning.
Experience with modern causal modeling techniques and libraries such as EconML, DoWhy, CausalML, PyMC, statsmodels, or equivalent tools.
Familiarity with model lifecycle and 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 will be considered.

What Success Looks Like

Establish reliable, multi-horizon forecasts for critical Success and business metrics.
Provide Success leadership with scenario-modeling capabilities used to evaluate customer strategies, investments, and resource decisions.
Enable credible measurement of customer interventions against baseline retention, adoption, expansion, and value-realization performance.
Identify the leading indicators and causal drivers of customer outcomes so teams can take earlier and more targeted action.
Integrate models into Success planning and operating workflows so analytical insights lead to measurable action.
Build trusted, scalable capabilities that demonstrate sustained adoption and measurable customer and business impact.

The base pay range for this position varies based on the geographic location. More information about the pay range specific to candidate location and other factors will be shared during the recruitment process. Individual pay is determined based on location of residence and multiple factors, including job-related knowledge, skills and experience.

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As a part of the total compensation package, this role may be eligible for a bonus. For information on our benefits click here.

About employer

More about this job

Skills

Data analysis
Decision making
Forecasting
Data engineering
Machine learning
Python
pandas
NumPy
Scikit-learn
SQL
Snowflake
Estimating
Statistics
Communication skills
Develop models
Customer segmentation
Prioritization
Reporting
Computer science
Research
Leadership
Recruitment

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