In-depth understanding of Statistics and probability concepts
Expertise in Machine Learning and deep learning libraries such as Scikit-learn, Keras, Tensorflow, PyTorch, etc.
Deep understanding of supervised and unsupervised ML algorithms like Logistic and Linear Regression, Gradient Descent, Decision Trees, SVM, Naive Bayes, Bagging and boosting, K-Means Clustering, Hierarchical clustering, PCA, etc.
Knowledge of deep learning concepts like Neural Networks, CNNs, RNNs, Backpropagation, Attention, Transformers, etc.
Experience working with LLMs like LLAMA, GPT, etc.
Experience with big data tools: Hadoop, Spark, Kafka, etc.
Experience with AWS cloud services: EC2, EMR, Redshift, SageMaker
Experience with Flask, and MLflow for deploying and tracking ML models.
Knowledge of building LLMs applications with frameworks like Langchain/Llama-index is plus
Responsibilities
Conducting extensive ML research and developing new ML use cases for products.
Mentoring Junior data scientists and Interns to deliver data science projects
Conducting Exploratory Data Analysis (EDA) on various datasets to identify trends and patterns
Utilizing a wide range of Machine Learning and deep learning techniques to solve complex business problems
Creating, maintaining, and scaling data pipelines to process large amounts of data
Perform ad-hoc analyses of data stored in MySQL/Postgres databases and writes SQL scripts, stored procedures, functions, and views
Deploying machine learning models and data pipelines to the cloud using various tools such as SageMaker, Flask, EC2, etc.
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Data ScientistEmployement Type: Full time