Job
Description
As a Machine Learning Engineer at Lifease Solutions LLP, your role involves working on predictive modeling and deep learning for cricket analytics. You will be responsible for developing ML/DL models to predict match scores, outcomes, team & player performances, and statistics. Your key responsibilities include: - Developing ML/DL models for predicting match scores & outcomes, team & player performances, and statistics. - Implementing time-series forecasting models (LSTMs, Transformers, ARIMA, etc.) for score predictions. - Training and fine-tuning reinforcement learning models for strategic cricket decision-making. - Developing ensemble learning techniques to improve predictive accuracy. In addition to predictive modeling, you will also work on in-depth cricket analytics which includes: - Designing models to analyze player form, team strengths, matchups, and opposition weaknesses. - Building player impact and performance forecasting models based on pitch conditions, opposition, and recent form. - Extracting insights from match footage and live tracking data using deep learning-based video analytics. Your role will also involve data processing & engineering tasks such as: - Collecting, cleaning, and preprocessing structured and unstructured cricket datasets from APIs, scorecards, and video feeds. - Building data pipelines (ETL) for real-time and historical data ingestion. - Working with large-scale datasets using big data tools (Spark, Hadoop, Dask, etc.). As part of model deployment & MLOps, you will be responsible for: - Deploying ML/DL models into production environments (AWS, GCP, Azure etc). - Developing APIs to serve predictive models for real-time applications. - Implementing CI/CD pipelines, model monitoring, and retraining workflows for continuous improvement. You will also work on performance metrics & model explainability by: - Defining and optimizing evaluation metrics (MAE, RMSE, ROC-AUC, etc.) for model performance tracking. - Implementing explainable AI techniques to improve model transparency. - Continuously updating models with new match data, player form & injuries, and team form changes. About Lifease Solutions LLP: Lifease Solutions LLP is a leading provider of software solutions and services based in Noida, India. The company believes in the power of design and technology to solve problems and bring ideas to life. With expertise in finance, sports, and capital market domains, Lifease Solutions is committed to delivering innovative solutions that drive value and growth for its customers globally. The company's success lies in its ability to turn small projects into significant achievements and help clients maximize their IT investments. As a Machine Learning Engineer at Lifease Solutions LLP, your role involves working on predictive modeling and deep learning for cricket analytics. You will be responsible for developing ML/DL models to predict match scores, outcomes, team & player performances, and statistics. Your key responsibilities include: - Developing ML/DL models for predicting match scores & outcomes, team & player performances, and statistics. - Implementing time-series forecasting models (LSTMs, Transformers, ARIMA, etc.) for score predictions. - Training and fine-tuning reinforcement learning models for strategic cricket decision-making. - Developing ensemble learning techniques to improve predictive accuracy. In addition to predictive modeling, you will also work on in-depth cricket analytics which includes: - Designing models to analyze player form, team strengths, matchups, and opposition weaknesses. - Building player impact and performance forecasting models based on pitch conditions, opposition, and recent form. - Extracting insights from match footage and live tracking data using deep learning-based video analytics. Your role will also involve data processing & engineering tasks such as: - Collecting, cleaning, and preprocessing structured and unstructured cricket datasets from APIs, scorecards, and video feeds. - Building data pipelines (ETL) for real-time and historical data ingestion. - Working with large-scale datasets using big data tools (Spark, Hadoop, Dask, etc.). As part of model deployment & MLOps, you will be responsible for: - Deploying ML/DL models into production environments (AWS, GCP, Azure etc). - Developing APIs to serve predictive models for real-time applications. - Implementing CI/CD pipelines, model monitoring, and retraining workflows for continuous improvement. You will also work on performance metrics & model explainability by: - Defining and optimizing evaluation metrics (MAE, RMSE, ROC-AUC, etc.) for model performance tracking. - Implementing explainable AI techniques to improve model transparency. - Continuously updating models with new match data, player form & injuries, and team form changes. About Lifease Solutions LLP: Lifease Solutions LLP is a leading provider of software solutions and services based in Noida, India.
