{"id":959436,"url":"https://alion.io/job/diesel-laptops-principal-machine-learning-scientist","title":"Principal Machine Learning Scientist","company":{"id":692875,"name":"Diesel Laptops","domain":"diesellaptops.com","url":"https://alion.io/company/diesellaptops","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Paycom","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"SciPy","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false},{"name":"Anomaly Detection","optional":true},{"name":"Apache Iceberg","optional":true},{"name":"ClickHouse","optional":true},{"name":"Dagster","optional":true},{"name":"dbt","optional":true},{"name":"Docker","optional":true},{"name":"Flink","optional":true},{"name":"Git","optional":true},{"name":"GitHub","optional":true},{"name":"MLFlow","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-09-16T08:24:07Z","employer_posted_date":"2026-09-16","last_verified_at":"2026-11-06T04:49:08Z","board_verified":true,"closed_at":null,"days_open":51,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":51},"description":"Job Title: Principal Data Scientist, Vehicle Analytics\nCompany Overview\nDiesel Laptops is a leading provider of diagnostic tools, repair information, software, training, and technology solutions for the commercial truck and off-highway vehicle repair industry. We help repair facilities, fleets, technicians, and industry partners reduce downtime, improve repair accuracy, and make better operational decisions.\nWe are seeking a Principal Data Scientist, Vehicle Analytics, to help transform large volumes of vehicle telemetry, service history, repair outcomes, and operational data into meaningful insights, predictive capabilities, and production-ready analytical solutions.\nPosition Summary\nThe Principal Data Scientist, Vehicle Analytics, is a senior individual contributor responsible for solving complex business, vehicle, and engineering problems through statistical analysis, experimentation, predictive modeling, and applied data science.\nThis role partners closely with Software Engineering, Data Engineering, Product, Remote Solutions Engineering, and customer-facing teams to identify high-value problems, define analytical approaches, validate hypotheses, and deploy reliable data-driven capabilities.\nMachine learning is an important part of the role, but success is defined by selecting the most appropriate analytical method for each problem rather than applying machine learning where a simpler statistical or analytical approach would be more reliable, explainable, or useful.\nThis position does not have routine direct reports but is expected to provide scientific leadership, technical mentorship, and guidance across the organization.\nKey Responsibilities\nData Analysis and Scientific Problem Solving\nInvestigate complex business, vehicle, and engineering problems using exploratory data analysis, statistical analysis, experimentation, and hypothesis testing.\nAnalyze vehicle telemetry, time-series data, fault codes, service history, repair outcomes, and operational data.\nTranslate ambiguous customer and business questions into measurable hypotheses, analytical plans, and actionable recommendations.\nIdentify trends, anomalies, failure patterns, and operational drivers that affect vehicle reliability, maintenance, and customer outcomes.\nPresent findings, limitations, uncertainty, and recommendations to technical and nontechnical stakeholders.\nStatistical Modeling and Machine Learning\nDesign, develop, validate, and improve statistical models, anomaly-detection methods, predictive-maintenance models, classification systems, and related analytical solutions.\nDetermine whether statistical analysis, machine learning, experimentation, or another analytical method is most appropriate for the problem.\nDefine and monitor performance measures such as precision, recall, F1 score, false-positive rate, stability, latency, and business impact.\nDocument assumptions, methodology, validation results, limitations, and performance findings to ensure reproducibility and transparency.\nMonitor deployed models and analyses and recommend retraining, redesign, or retirement when appropriate.\nData Products and Production Systems\nBuild production-ready analytical workflows, contextual tools, reports, prototypes, and model components.\nPartner with Data Engineering and Software Engineering to productionize analyses and models using reliable pipelines, APIs, testing, observability, and deployment practices.\nContribute code and technical documentation using approved engineering standards.\nSupport testing, validation, monitoring, and continuous improvement of production data-science solutions.\nEnsure analytical work is auditable, reproducible, maintainable, and appropriately documented.\nCollaboration and Technical Leadership\nPartner with Product, Engineering, Remote Solutions Engineering, Customer Success, and business leaders to identify and prioritize analytical opportunities.\nParticipate in technical design discussions, scientific reviews, code reviews, and model-validation reviews.\nMentor data scientists, analysts, and engineers in statistics, experimentation, analytical reasoning, and model evaluation.\nEstablish and promote best practices for analytical quality, reproducibility, documentation, and responsible model use.\nCommunicate scientific findings and recommendations to executives, customers, and other stakeholders when required.\nQualifications\nRequired Qualifications\nMaster’s degree in Data Science, Statistics, Computer Science, Applied Mathematics, Engineering, or a related quantitative field, or equivalent advanced professional experience.\nSeven or more years of progressive experience in applied data science, statistical modeling, machine learning, or quantitative research.\nDemonstrated experience solving complex problems using large, imperfect, high-volume, or time-series datasets.\nAdvanced proficiency with Python and SQL.\nStrong experience with Pandas, NumPy, SciPy, and Jupyter.\nStrong foundation in statistics, experimental design, hypothesis testing, model validation, and communication of uncertainty.\nExperience developing and deploying production data-science or machine-learning solutions.\nAbility to independently define methodology, evaluate technical tradeoffs, and lead complex analytical initiatives.\nStrong written and verbal communication skills.\nPreferred Qualifications\nPhD in Data Science, Statistics, Computer Science, Applied Mathematics, Engineering, or a related quantitative field.\nExperience with vehicle telemetry, IoT, connected-device, fleet, transportation, predictive-maintenance, or industrial time-series data.\nExperience with anomaly detection, equipment-failure prediction, maintenance optimization, natural-language processing, or large language models.\nExperience with dbt, Dagster, Apache Flink, ClickHouse, PostgreSQL, Apache Iceberg, Docker, and MLflow.\nExperience producing statistically valid customer-facing analyses, technical case studies, or research reports.\nPublication, patent, or significant applied-research experience.\nCore Technologies\nPython\nSQL\nPandas\nNumPy\nSciPy\nJupyter Notebooks\ndbt\nDagster\nApache Flink\nClickHouse\nPostgreSQL\nApache Iceberg\nGit\nGitHub\nDocker\nMLflow\nPosition Details\nFull-time\nExempt\nPrincipal individual-contributor role\nRemote within the United States, with hybrid eligibility in Columbia, South Carolina\nOccasional travel may be required","description_format":"text","description_chars":6373,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Test & Measurement Instruments","Maintenance, Repair & Operations (MRO)","Automotive 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