Senior Data Scientist, Data Analytics

🔥 1 minute ago

🌐 United States, Canada – Remote

🇺🇸 United States – Remote 🇨🇦 Canada – Remote

⏰ Full Time

🟠 Senior

📉 Data Analyst

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Calix

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2000

📡 Telecommunications

☁️ SaaS

🏢 Enterprise

💰 $50M Venture Round on 2009-08

Telecommunications • SaaS • Enterprise

Calix is a comprehensive solutions provider that focuses on enabling broadband service providers (BSPs) to simplify, innovate, and grow their businesses. Through its advanced broadband platform, Calix offers technologies like 10G-PON, 5G, Wi-Fi 7, and more to improve network operations, reduce downtime, and enhance the subscriber experience. The company provides managed services such as SmartLife and SmartHome, which help subscribers operate, secure, and enhance their connected lifestyles. Calix serves diverse provider types including telcos, cable operators, and municipal utilities, helping them deliver critical broadband connectivity and transform community access to digital services. With a focus on cloud technology, analytics, and transformation guidance, Calix empowers service providers to thrive in the digital age.

📋 Description

• Design and develop robust time-series forecasting models for revenue, retention, churn, expansion, customer adoption, and operational demand • Partner with Success leaders to identify drivers of customer outcomes and develop analytical capabilities supporting strategic and operational objectives • Build simulation and scenario-modeling frameworks integrating forecasts, business constraints, and causal effects • Apply causal inference, experimentation, and uplift modeling to measure customer and business interventions against baseline trends • Develop Bayesian and probabilistic models that quantify uncertainty and provide decision ranges • Use enterprise data to train, back-test, validate, and continuously improve models • Manage the end-to-end model lifecycle, including problem framing, feature engineering, development, deployment, monitoring, and tuning • Partner with data engineering and platform teams to deploy scalable models integrated with enterprise data systems and Success workflows • Establish evaluation practices including back-testing, sensitivity analysis, explainability, and impact measurement • Translate customer and business dynamics into scenarios, insights, and recommendations aligned to leadership decision points • Establish reliable multi-horizon forecasts for critical Success and business metrics • Provide leadership with scenario-modeling capabilities for evaluating customer strategies, investments, and resource decisions • Enable measurement of customer interventions against baseline retention, adoption, expansion, and value-realization performance • Identify leading indicators and causal drivers of customer outcomes • Integrate models into Success planning and operating workflows to drive measurable action • Build trusted, scalable capabilities demonstrating sustained adoption and measurable customer and business impact

🎯 Requirements

• 7+ years of experience in data science, decision science, applied machine learning, statistical modeling, or advanced analytics in production environments • Advanced Python skills, including 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, such as Monte Carlo models • 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 • Ability to translate ambiguous business problems into structured mathematical or statistical models • Experience integrating models into planning or operational workflows used to make business decisions • Strong communication skills, including explaining methods, limitations, uncertainty, and recommendations to business and executive stakeholders • Preferred: experience in subscription, SaaS, telecommunications, customer success, or customer lifecycle analytics • Preferred: familiarity with NRR, retention, churn, expansion, product adoption, customer health, and value-realization metrics • Preferred: experience developing models supporting customer segmentation, risk identification, intervention prioritization, or expansion planning • Preferred: experience with EconML, DoWhy, CausalML, PyMC, statsmodels, or equivalent tools • Preferred: familiarity with MLOps practices for deployment, reproducibility, monitoring, and governance • Preferred: 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

🏖️ Benefits

• Bonus eligibility as part of the total compensation package • Benefits package (specific benefits linked in the posting)

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