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Primary Objectives of Position
To lead the development and deployment of advanced analytics and AI solutions to enhance and support public transport operations, asset reliability, maintenance optimization and commuter experience.
To serve as both a technical authority and business partner, translating data into actionable intelligence and measurable operational impact.
Major Responsibilities
Lead the end-to-end lifecycle of Machine Learning (ML) and AI projects, from business problem definition, data exploration, feature engineering, model training, validation to deployment and performance monitoring.
Develop and implement ML, AI or optimization models to address a range of business and operational challenges, such as improving performance, forecasting demand, optimizing resource utilization, enhancing reliability, or supporting better customer outcomes.
Collaborate with data and software engineers to deploy ML/AI models in both air-gapped and cloud production environments.
Drive experimentation and continuous learning using AI techniques such as time-series forecasting, anomaly detection, Computer Vision and Natural Language.
Present data-driven insights and recommendations in actionable business terms to business users.
Work with Business Analysts to engage users to identify and evaluate the feasibility of high-impact AI use cases within company Transit.
Manage AI projects and maintain AI infrastructure.
Job Specifications
Bachelors or Masters Degree in computer science, computer engineering, statistics, data analytics, a pplied mathematics or a relevant field.
At least 8 years relevant data science experience, including 2 or more years in a lead or senior technical role.
Proficiency in Python, Nifi, Airflow, SQL, libraries such as pandas, numpy, scikit-learn, TensorFlow, PyTorch, and MLOps tools such as Databricks, Snowflake, AWS Sagemaker.
Proficiency in Data Visualization tools like Power BI, Qlik and Tableau
Job ID: 153935431
Skills:
Mqtt, Amqp, Databricks, AWS, data pipelines, GenAI use cases, retrieval-augmented generation, robotics data formats, analytics layer, Data Collection, OPC-UA, large language model, ML models, data models, backend services, robotics telemetry
Skills:
Modern data platform tooling (e.g. Databricks or equivalent), Robotics telemetry, Data ingestion, AWS or comparable cloud infrastructure, Data pipelines, data models, Robotics data formats, ML models, Backend services
Skills:
Data Scientist, Sql, Python
Skills:
Machine Learning, Hadoop, Sql, Deep Learning, Tensorflow, Data Quality, Pytorch, Gcp, MLops, Spark, Data Governance, Predictive Analytics, Azure, Python, AWS, Generative AI, LLMOps, Scikit-learn, reinforcement learning, Model Deployment
Skills:
causal inference , Machine Learning, Sql, Git, Docker, Python, Statistics, Modelling Libraries, Jupyter, Data Analysis, Simulation, Econometrics