* Conducting the full end-to-end machine learning cycle from model development to model deployment.* Maintaining the full CI-CD production pipeline through AWS SageMaker, Step Function,Lambda, Github, Terraform, Jenkins.* Developing machine learning models to predict the student success via a multitude ofpredictors using miscellaneous machine learning models such as XGBoost, Logistic Regressionetc.* Performing the model interpretability analysis using Shapley Additive exPlanations(SHAP) for a better understanding of the machine learning models.* Conducting training sessions for the team members to get familiar with the productionpipeline.* Presenting the data analysis findings and holding training sessions for the team memberswith an end-goal of brainstorming on future directions and explorations.* Presenting the model development and deployment process outside of our team withindifferent research groups.

* Devising mathematical framework for optimal budget allocation using multi-variateconstraint optimization techniques.* Working closely with other Data Scientists/Machine Learning scientists to develop,fine-tune and deploy the machine learning models for production.* Working with tables with millions of rows including querying, filtering and miningthe data towards finding the correlation between the fields with an end-goal of defininga business problem.* Collaborating closely with the project manager to brainstorm and translate thebusiness problems into machine learning solutions.

* Developing machine/deep learning architectures to emulate the conventional hydrological models* Working on big and fast data analytics for surface and subsurface model and data integration * Design and optimization of big and fast data analytics systems as applied to collecting, storing and processing field data relevant to monitoring trends and underlying interdependencies in surface and underground water ecosystems across large geographical areas * Benchmarking large complex analytics workloads -Innovating in popular big data systems, such as Hadoop or Spark, with new algorithms for analysis of complex water ecosystems * Optimization under multiple objectives including latency, throughput, and cloud computing cost

* Pioneering novel algorithms for HRTF personalisation* Implementing algorithms for feature extraction from images* Collaborating with the acoustics team to build morphological models* Collaborating with the measurements and the acoustic team to verify predicted HRTFs with that of measured and simulated results* Designing and building the infrastructure with the engineering team to deliver immersive auditory experiences * Drafting intellectual property