Lead Data Scientist

Primary Responsibilities:

• Data scientists analyze and interpret complex data to help organizations make informed decisions
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regard to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Required Qualifications:

• AI Scientist
• Masters or Relavant in Computer Science, Machine Learning, or related field
• 8+ years of experience in AIML engineering and research
• Experience with experiment tracking tools (e.g., Weights & Biases, MLflow)
• Hands-on experience with MLOps, model deployment, and monitoring
• Proven expertise in LLMs, generative AI, and deep learning
• Solid programming skills in Python and familiarity with ML libraries (e.g., scikit-learn, Keras)
• Familiarity with AIML governance, ethics, and responsible AI practices

Key Skills:

• Proficiency AutoML: Automated Machine Learning (AutoML) tools like H2O.ai, Google Cloud AutoML, and DataRobot
• Solid statistical and mathematical knowledge
• Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as
• Python, R, and SQL
• Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil
• Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks - Feedforward, CNN, LSTMs GRUs is a plus.
• Optimization techniques - Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost Functions in Neural Nets - Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing
• Deep Learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono
• Exposure or experience using collaboration tools such as: Confluence (Documentation)
• Synthetic Data Generation: Tools like Gretel.ai and Synthea are used to generate synthetic data, which can be useful for training models when real data is scarce or sensitive