Principal Data Scientist, Customer Analytics

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

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401(k) match

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

55% above the BLS national median for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

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About this role

Principal Data Scientist, Customer Analytics Cambridge, MA The Principal Data Scientist is a senior individual contributor and technical leader on the Customer Analytics team. This role leads the development and delivery of customer-facing analytics and data science solutions, with a focus on scientific rigor, scalable implementation, and measurable business impact. The position partners closely with Customer Solutions, Data Engineering, and Customer Success to translate complex business challenges into production-grade analytics, machine learning, and AI capabilities that improve product outcomes and client value. CMT is looking for a Principal Data Scientist I, Customer Analytics to help us change the world. CMT has helped protect over 65 million drivers and prevent over 126,000 crashes worldwide. We build AI to solve some of the most difficult challenges in mobility - understanding and reducing risk, detecting crashes, and getting people life-saving help. The problems are hard. The impact is real. No matter your role, your work will matter at CMT. Responsibilities: - Serve as a technical lead with a deep understanding of modeling methods from underlying mathematics, to assumptions, tradeoffs, interpretability, and limitations to ensure results are robust and reliable. - Use independent judgment and discretion to frame ambiguous business and customer problems into well-defined data science problem statements, project plans, modeling approaches, and measurable success metrics - Develop and implement advanced statistical and machine learning models end-to-end, including data pipelines, feature engineering, model development, validation, deployment, and ongoing monitoring - Partner with analysts to build reporting dashboards and data products that convert model

Read the full description at www.cmtelematics.com. FewerJobs shows a source-linked preview and links to the original posting.