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Data Scientist
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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: 153660431
Skills:
Machine Learning, Google Cloud, Sql, Deep Learning, Sklearn, Tensorflow, Numpy, Pandas, MLops, Pytorch, Ibm, Docker, Azure, Python, Kubernetes, AWS, LLMOps tooling, Polars, Time-series Econometric modeling, Gen-AI applications
Skills:
model development , Mqtt, Hypothesis Testing, Amqp, Databricks, AWS, data pipelines, GenAI use cases, retrieval-augmented generation, robotics data formats, analytics layer, Data Collection, OPC-UA, ML models, data models, pipeline design, ingestion pipelines, backend services, robotics telemetry
Skills:
Mqtt, Amqp, Databricks, AWS, data pipelines, GenAI use cases, retrieval-augmented generation, robotics data formats, Data Collection, analytics layer, large language model, OPC-UA, ML models, data models, backend services, robotics telemetry
Skills:
Machine Learning, Data Cleaning, Dashboards, Agile Project Management, Powerbi, Data Visualisation, Python, model deployment, pre-processing, data insights, data science models, data quality issues, R, interactive visualisations, statistical methodologies, Stakeholder Management, feature engineering
Skills:
Scipy, Pyspark, Sql, Numpy, Git, Pandas, XGBoost, Python, hyper-parameter optimization, pipeline development, Visualization, Amazon Quick Sight, Data Processing, object-oriented programming, data platforms, CI CD, Scikit-Learn, Transformation, model validation, test-driven development, AWS SageMaker, data ingestion, feature selection
