Lead Data Scientist - Digital Elevator Solutions

TK Elevator is seeking a Lead Data Scientist for Digital Elevator Solutions in Atlanta, GA. This role combines deep, hands-on data science expertise with technical leadership and strong business acumen within TK Elevator’s North America Digital Operations Center (DOC).

About the role

The Lead Data Scientist will transform IoT, service, and operational data into advanced analytics and AI-driven solutions to improve equipment reliability, field productivity, service delivery, and customer outcomes. This role involves leading the development and deployment of machine learning, predictive analytics, and AI solutions using data from connected elevators and escalators, including equipment movements, sensor data, error codes, service history, and other operational information.

The successful candidate will collaborate with Field Operations, Engineering, Product Management, R&D, Data Engineering, and Digital teams to identify operational challenges and translate them into scalable, data-driven solutions with measurable business value. As part of a global Digital Operations Center network, this role will also work with counterparts across multiple global locations to leverage and scale solutions.

Responsibilities

• Lead the design, development, deployment, and optimization of machine learning models, predictive analytics, and AI solutions supporting connected elevator and escalator technologies.
• Analyze IoT sensor streams, equipment movements, error codes, service history, and other operational data to predict service needs and potential equipment failures.
• Develop predictive maintenance, anomaly detection, classification, regression, and time-series forecasting models to improve equipment reliability, uptime, and serviceability.
• Analyze large and complex datasets to identify trends, patterns, anomalies, and opportunities to improve field productivity, service quality, and customer outcomes.
• Translate operational and business challenges into scalable analytical solutions with clearly defined success metrics and measurable business impact.
• Manage and contribute to the full machine learning lifecycle, from data exploration and model development through validation, deployment, monitoring, and continuous improvement.
• Drive the development and advancement of data-driven digital capabilities, including predictive maintenance, remote monitoring, AI-driven fault detection, equipment performance analytics, field service optimization, connected equipment solutions, customer insights, and digital twin technologies.
• Develop solutions that provide field technicians with relevant, contextual equipment and service information before, during, and after maintenance activities.
• Partner with Field Operations, Product Management, Engineering, and R&D teams to identify high-value use cases and develop solutions addressing real-world service and equipment challenges.
• Support the development of analytics, insights, and tools that improve decision-making for field teams, operational leaders, and customers.
• Develop and implement AI and machine learning solutions using techniques such as time-series forecasting, anomaly detection, classification, regression, predictive analytics, statistical modeling, natural language processing, generative AI, and deep learning.
• Develop and refine prompts, context, data inputs, and supporting frameworks for GenAI and LLM-based applications supporting field service and digital solutions.
• Evaluate emerging AI and machine learning technologies and identify practical opportunities to apply them within elevator service, field operations, and connected equipment.
• Establish and promote best practices for model development, validation, deployment, monitoring, governance, and responsible use of AI.
• Serve as a technical leader for data science, AI, and advanced analytics initiatives within the North America Digital Operations Center.
• Lead, mentor, and develop data science and analytics professionals while remaining actively engaged in hands-on technical work.
• Identify and prioritize high-value analytics, machine learning, and AI use cases based on operational needs, feasibility, scalability, and potential business impact.
• Define success metrics and value-tracking approaches to quantify the operational and financial impact of data-driven solutions.
• Provide recommendations to Digital and Field Operations leadership regarding opportunities to leverage data, analytics, and AI to improve service delivery and operational performance.
• Communicate complex technical concepts, analytical findings, and business impact clearly to technical and non-technical stakeholders.
• Promote a culture of experimentation, innovation, collaboration, and measurable value creation.
• Collaborate closely with Data Science, Digital, Engineering, R&D, and Digital Operations Center counterparts across TK Elevator's global organization.
• Share methodologies, models, lessons learned, and best practices across regions to accelerate innovation and reduce duplication of effort.
• Evaluate solutions developed by global teams for applicability within North America and lead efforts to adapt and implement proven capabilities.
• Design solutions with scalability in mind, enabling successful North American use cases to be adopted by other regions and global teams.
• Work hands-on with Python and SQL in modern cloud-based data and analytics environments such as Databricks.
• Collaborate with data engineers and architects to transform raw and curated data into reliable, production-ready datasets for analytics, AI, and machine learning applications.
• Support the development of scalable analytical frameworks and data pipelines required for production data science solutions.
• Ensure appropriate data quality, model integrity, documentation, and governance throughout the analytics lifecycle.
• Develop or support dashboards and visualizations that effectively communicate analytical findings, solution performance, and business impact.

Requirements

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field required; Master's degree preferred.
• 7+ years of experience in data science, machine learning, advanced analytics, data engineering, or a related data discipline, with increasing levels of responsibility.
• Demonstrated experience developing and deploying machine learning or predictive analytics solutions in production environments.
• Experience leading complex analytics, machine learning, or AI initiatives and mentoring or developing technical professionals.
• Demonstrated ability to translate data and analytical findings into measurable operational or business improvements.
• Experience working with large, complex datasets; experience with IoT, telemetry, connected devices, industrial equipment, or field service data is highly preferred.

Skills

• Strong hands-on proficiency in Python and SQL.
• Experience working with modern cloud-based data and analytics platforms such as Databricks, Azure, AWS, or similar environments.
• Strong knowledge of machine learning techniques, including regression, classification, anomaly detection, predictive modeling, and time-series forecasting.
• Experience developing and deploying machine learning models across the full model lifecycle.
• Experience with AI and Generative AI applications, including practical experience developing or supporting LLM-based solutions.
• Familiarity with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, or comparable technologies.
• Experience working collaboratively with data engineering teams and production data pipelines.
• Familiarity with data visualization and business intelligence platforms such as Power BI or Tableau.

Preferred Qualifications

• Experience within the elevator, industrial services, manufacturing, connected equipment, smart building, or other asset-intensive industries.
• Knowledge of predictive maintenance, equipment reliability, remote monitoring, or field service optimization.
• Experience working with digital products, IoT platforms, or connected service offerings.
• Understanding of field service management processes and service-based business models.
• Experience working within a global organization and collaborating across regions, cultures, and technical teams.

Benefits

• Medical, dental, and vision coverage (eligibility requirements apply).
• Flexible spending accounts (FSA).
• Health savings account (HSA).
• Supplemental medical plans.
• Company-paid short- and long-term disability insurance.
• Company-paid basic life insurance and AD&D.
• Optional life and AD&D coverage, including spouse and dependent life insurance.
• Identity theft monitoring.
• Pet insurance.
• Company-paid Employee Assistance Program (EAP).
• Tuition reimbursement.
• 401(k) Retirement Savings Plan with company match (dollar-for-dollar on the first 5% contributed).
• 15 days of vacation per year.
• 11 paid holidays each calendar year (10 fixed, 1 floating).
• Paid sick leave, per company policy.
• Up to six weeks of paid parental leave (available after 90 days of full-time employment).