Title: Data Scientist (2008) Location: Sacramento CA US Hybrid Job Description: About Kooner Fleet Management Solutions Kooner Fleet Management Solutions is one of the fastest-growing national providers of on-site fleet maintenance, preventative service, and mobile repair solutions. With nearly a decade of industry experience, Kooner FMS helps fleets reduce downtime, extend vehicle life, and simplify operations through proactive maintenance programs and advanced technology. As a family-founded and field-first company, we take pride in delivering trusted partnerships, exceptional service, and innovative fleet management solutions that keep America’s trucks and trailers road-ready. About the Data Scientist Role We are looking for a Data Scientist to serve as our in-house analytical authority, reporting directly to the CFO, embedded with the executive team, and working across every department to surface the insights that drive how we run and grow the business. This is not a reporting role. This person builds the infrastructure and intelligence layer between our data systems and our decision-making. They don't just find trends, they architect the pipelines, models, and systems that make those trends visible at scale. They find the money we are leaving on the table, identify where we are operating inefficiently, and deliver clear recommendations backed by rigorous statistical modeling – not just charts. This is a hybrid opportunity based out of our corporate office in Sacramento, CA. Where You’ll Make an Impact - Connect and analyze data across FleetIQ, Samsara GPS, Rippling, time schedules, QuickBooks, and Power BI to build a unified view of business performance - Identify cost-saving opportunities and operational inefficiencies across departments; workforce, fleet, dispatch, finance, and field operations - Develop and maintain dashboards and scorecards in Power BI and Tableau that give leadership and department heads real-time visibility into KPIs - Analyze technician profitability, labor cost vs. output, overtime patterns, and timecard compliance - Evaluate GPS and fleet data against clock records to detect off-clock vehicle use, idle time, and route inefficiency - Assess dispatch metrics - time to first assignment, technician response times, job completion rates - and identify structural improvements - Leverage AI tools and prompt engineering to accelerate analysis, automate summaries, and enhance the depth of insight from existing data - Build and maintain master mapping tables to normalize technician names, truck assignments, territory codes, and cross-system identifiers - Partner with the CFO and executive team to frame business questions analytically and return with data-backed recommendations - Proactively surface trends, anomalies, and risks the business is not yet measuring before they become problems What a Strong Performance Looks Like - 60 days: You have audited all key data sources, mapped their schemas and relationships, stood up a data warehouse environment, and delivered a first cross-department performance view to the CFO. - 6 months: Automated data pipelines are running in production. Dashboards are self-refreshing. You have identified and quantified at least three material cost-saving or efficiency opportunities with model-backed recommended actions. - 12 months: A production ML model is influencing at least one operational process. Data is embedded in how the company makes decisions. Leaders are citing your analyses in operational and financial planning. What Makes You a Great Fit Required Experience - 8–10 years minimum in data science, data engineering, or a senior analytical role with a strong engineering component - Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or related quantitative field required; Master's degree preferred - Background in field service, logistics, last-mile delivery, fleet operations, or a similarly data-rich operational business strongly preferred - Demonstrated history of translating data findings into business decisions, not just reports - Experience working directly with finance leadership or C-suite as an analytical partner - Proven ability to integrate data across multiple enterprise platforms with inconsistent formats and naming conventions Technical Skills - Expert-level SQL – complex joins, window functions, CTEs, query optimization, and working with multi-source messy data at scale - Python at a production level – not just pandas scripts, but well-structured, tested, version-controlled code (OOP, virtual environments, packaging) - Power BI and Tableau for production-grade dashboards used by non-technical stakeholders - Excel at an advanced level - pivot tables, complex formulas, model building - Proficient with AI tools and prompt engineering to accelerate insight generation and automate routine analysis tasks Communication & Collaboration - Exceptional verbal and written communication - able to explain a complex finding in two sentences to an executive - Comfort working cross-functionally across operations, HR, finance, fleet, and dispatch - Self-directed and proactive - you identify what needs to be measured, not just what you are asked to measure - Comfortable presenting findings that are uncomfortable - the data tells the truth; you communicate it clearly and constructively Why You’ll Love Joining Our Team - Earn What You Deserve: Competitive pay starting at $120K-$150K based on experience. - Weekly Paydays: Get paid every Friday – no waiting around! - Invest in Your Future: 401(k) with company match. - Health Benefits that Kick in Fast: Medical, Dental, and Vision coverage after just 30 Days! - Time to Recharge: Enjoy paid vacation time, paid sick time, and paid holidays to rest, recharge, and take care of what matters most. - We’ve Got You Covered: Life & Disability Insurance for added peace of mind – because we take care of our team on and off the job. - We’re Here for You: Access to our Employee Assistance Program for support when you need it most – we've got your back. - Grow With Us: Big Career Growth Opportunities in a rapidly expanding, forward-thinking organization. Work Environment - Standard office setting - Must be able to lift up to 10 lbs - Must be able to sit for up to 4 hours at a time Kooner Fleet Management Solutions is an Equal Opportunity Employer. We are committed to creating an inclusive environment for all employees and applicants, free from discrimination and harassment. All qualified candidates will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law. We celebrate diversity and are dedicated to fostering a workplace where every team member can thrive.

Title: Data Scientist (Remote) Location: Corning, NY, US, 14831 Workplace: remote Department: Accounting/Finance/Planning/Audit Job Description: Requisition Number: 74528 The company built on breakthroughs. ​  Join us.​  Corning is one of the world’s leading innovators in glass, ceramic, and materials science. From the depths of the ocean to the farthest reaches of space, our technologies push the boundaries of what’s possible.  ​  How do we do this? With our people. They break through limitations and expectations – not once in a career, but every day. They help move our company, and the world, forward. ​  ​At Corning, there are endless possibilities for making an impact. You can help connect the unconnected, drive the future of automobiles, transform at-home entertainment, and ensure the delivery of lifesaving medicines. And so much more.​   Come break through with us.  Corning’s businesses are ever-evolving to best serve our customers, industries, and consumers. Today, we accelerate and transform life sciences, mobile consumer electronics, optical communications, display, automotive, and solar markets. We are changing the world with: - Trusted products that accelerate drug discovery, development, and delivery to save lives - Damage-resistant cover glass to enhance the devices that keep us connected - Optical fiber, wireless technologies, and connectivity solutions to carry information and ideas at the speed of light - Precision glass for advanced displays to deliver richer experiences - Auto glass and ceramics to drive cleaner, safer, and smarter transportation - Solar polysilicon, wafers, and innovative photovoltaic modules, enabling low-cost solar energy solutions Location: Remote Role Summary The Data Scientist II / III role is an exciting opportunity to join Corning’s Data Science & Insight (DSI) team, where the individual will develop AI and machine learning solutions that enhance efficiency, generate actionable insights, and improve decision-making across a large and complex Fortune 500 organization. This position sits within the Finance function and supports digital transformation initiatives across both corporate finance and the broader enterprise. A central focus of the role is the design and delivery of enterprise-grade, reusable AI/ML models and frameworks that can be applied across finance to address a broad range of business challenges. The team brings together expertise in statistics, data science, machine learning, artificial intelligence, MLOps, and corporate finance. Projects are executed in a highly collaborative environment, while also requiring strong individual ownership and initiative. Using advanced analytical and modeling techniques, the Data Scientist will enable objective, insight-driven analysis for stakeholders at all levels, including senior leadership. This role requires deep technical capability in applying sophisticated data science and machine learning methods to complex finance-related challenges, including time series analysis, Bayesian modeling, supervised and unsupervised learning, reinforcement learning, deep learning, natural language processing, and Generative AI. The role is responsible for developing scalable, reusable solutions and helping to elevate modeling standards across the finance organization. This position follows a hybrid-remote model, with the expectation of being onsite at Corning headquarters for in-person meetings as needed. Role Context The Data Scientist is a core member of the centralized Digital Center AI team supporting Finance. This role is responsible for building and maintaining shared AI capabilities—including forecasting, predictive modeling, NLP/GenAI, prescriptive analytics, and pattern recognition—for use across FP&A, Treasury, Controllership, Tax, and Risk. Success in this role requires a strong focus on scalability, robustness, and responsible deployment, as well as the consistent application of industry best practices in model development, validation, documentation, governance, and MLOps. The individual in this role is also expected to remain current with advancements in AI and machine learning and translate relevant innovations into practical, enterprise-ready applications. Key Responsibilities - Design, develop, and validate foundational, reusable AI/ML models and frameworks that can be leveraged across multiple finance functions. - Apply advanced statistical and machine learning methods—including time series analysis, Bayesian techniques, tree-based models, clustering, deep learning, NLP, and Generative AI—to solve complex cross-functional finance business problems. - Implement best practices across the full model lifecycle, including problem framing, data quality assessment, feature engineering, validation, interpretability, monitoring, documentation, and reproducibility. - Evaluate existing models, metrics, and workflows critically, and recommend enhancements to improve robustness, scalability, and operational efficiency. - Partner with ML Engineers and Data Engineers to transition prototypes and research into production-ready, governed AI solutions. - Translate analytical findings into clear business insights and recommendations for senior finance leaders and executives. - Coach and mentor embedded Finance data scientists on modeling standards, reusable approaches, and best practices. - Stay informed on emerging AI/ML research, tools, and methodologies, and identify opportunities to adopt innovations that deliver measurable business value and can be operationalized responsibly. - Communicate learnings, model performance, and standards through presentations, documentation, and knowledge-sharing forums. - Compile, integrate, and prepare internal and external data sources for advanced analysis and modeling. - Contribute high-quality, well-documented code to shared repositories in accordance with enterprise standards. Required Education and Experience - Minimum of 5 years of experience applying data science and machine learning methods to solve complex business problems. - Master’s degree or PhD in a quantitative discipline such as Data Science, Statistics, Mathematics, Computer Science, Economics, or Finance. - Academic coursework in applied statistics, machine learning, or data science. - Coursework or demonstrated interest in Finance, Economics, or Operations Management is a plus. Required Qualifications - Demonstrated ability to work independently while contributing effectively within highly collaborative, cross-functional teams. - Proven success in converting research and analytical work into production-ready solutions. - Strong curiosity and willingness to challenge conventional processes and assumptions. - Self-motivated with a commitment to continuous learning and staying current with evolving AI/ML tools and practices. - Ability to communicate complex technical analysis clearly and effectively to senior business stakeholders. - Prior publications or conference presentations in quantitative or technical fields are a plus. Technical Competencies - Strong proficiency in Python and the broader Python AI/data science ecosystem. - Experience with Git-based source control, including platforms such as GitHub or GitLab. - Familiarity with Databricks and cloud-based machine learning platforms such as AWS or Azure is preferred. - Experience with distributed computing frameworks such as Spark is a plus. The range for this position is $109,335.00 - $150,336.00 assuming full time status. Starting pay for the successful applicant is dependent on a variety of job-related factors, including but not limited to geographic location, market demands, experience, training, and education.

• Collaborate with experienced modelers to build predictive models and analytic solutions using Python or R; apply techniques such as GLM, decision trees, GBM and NLP • Manipulate data using R or SQL Server; develop advanced ad hoc queries to investigate data anomalies and to summarize data for pattern detection • Develop Python or R functions and SQL stored procedures to automate recurring tasks • Create and maintain documentation associated with models • Assist in implementation and testing of models • Develop dashboards in Power BI to facilitate analyses that support modeling efforts or enable model usage • Monitor the performance and usage of models

• Analyze complex scientific data using statistics, machine learning, and data science methods • Build and improve workflows for data ingestion, cleaning, feature extraction, modeling, and reporting • Develop AI-enabled tools or workflow automation to support repeatable scientific analysis tasks • Partner with scientists, engineers, and operations teams to solve practical lab data problems • Build, test, validate, and document predictive or classification models for scientific applications • Perform exploratory analysis to identify trends, anomalies, and relationships in experimental data • Support automation and standardization of data analysis and reporting processes • Communicate results clearly to both technical and non-technical stakeholders • Contribute to scalable, reproducible data workflows that improve lab efficiency