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AMAR ****** Data Scientist

$600 / day
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Summary

Overall 2.5-year tenure as a Data Scientist, specialised in analysing complex datasets to drive strategic business decisions. Proficient in Python, SQL, Tableau, and advanced ML algorithms, excelling in analysing, visualising, and communicating insights. Aim to combine my data science skills with project management expertise to make impactful contributions.

SKills

3D Reconstruction and Registration (PCLand Calibration • Deep Learning and Neural Networks Architectures ◦ Convolutional Neural Networks (CNNAttentionBash and MATLAB)Machine Learning and Data analysis tools (TensorFlowBlender) • Nonlinear Optimization (C++ ceres and MANOPT for Optimization on Manifold) • Robotics platform (ROS and Gazebo) • Rigid and Non-Rigid SLAMC++ChatBotClassificationCloud CompareDecision TreesGRUKerasLanguages (PythonMachine Translation) • Supervised Machine Learning (RegressionMatplotlibMeshLabMobileNetNeural Style TransferNLP and Recurrent Neural Networks LSTMNumPyObject Detection and RecognitionOpen3DPandasPyMeshlabPyTorchRandom Forest and XGBoost) • UnsupResNetscikitlearnseaborn and scipy) • Computer Vision and Image Processing (OpenCV in Python and C++) • Point cloud ProcessingSemantic Segmentation) ◦ Sequence ModelsSpeech RecognitionSQLState Estimation and Pose Graph OptimizationTransfer LearningTransformersU-Net
AI ConsultantDeep Learning EngineerMachine Learning Engineer

Education

March 2019 - Dec 2020 Master of Data Science at The University of Melbourne
July 2014 - May 2018 Bachelor of Computer Science at VIT University

Experience

Oct 2021 - Present AI MODEL INDUSTRIALISATION at Veolia

– Implemented BurstID, an AI solution that optimizes pipe renewal plans to minimize water losses from networks, proving effective overall.
– Developed GreenPath Online, organisation’s solution for automatic monthly emissions tracking, enhancing awareness of carbon footprints in water treatment plants and supporting
targeted emission reduction plans for 2025.
– Innovated Virtual Submetering Trial, which divides energy consumption by equipment in facilities, reducing labor hours significantly compared to manual energy assessments
– Analysed data from water utilities to assist in customer profiling, identifying high-consuming customers, and helping them to run water-saving campaigns
– Accountable for code production, including constant maintenance of the code and re-factoring when necessary.

Mar 2020 - Dec 2020 Data Scientist at The University of Melbourne

– Identified key blockages in the University admissions data with their process time for international student course applications that take longer and have more transactions.
– Created efficient workflow combinations for the University to expedite the application process, ensuring minimal delays. Utilised dashboard visualisation to streamline
operations, resulting in a reduction from 8 to 7 weeks.
– Executed data exploration on a 1.5GB dataset to extract key features from 54 features in admission data. This involved indentifying correlations between various workflow
processes of appications and the corresponding completion times.
– Conducted daily, experiements involving data cleansing, processing and the application of Python-based Machine Learning classifiers. These efforts aimes to predict both the
likelihood of applications success post-offer release and the time taken by applicants to accept offers.