Data Scientist

• Conduct thorough exploratory data analysis (EDA) to uncover insights and inform model development.
• Hands on design, development, and deployment of predictive models, machine learning algorithms, and AI solutions.
• Collaborate with team members and business stakeholders to understand objectives and requirements and develop analytic models that provide actionable insights.
• Ensure the validity, reliability, and robustness of models by implementing best practices in model validation, testing, and calibration.
• Partner with the Advanced Analytics Center of Excellence to share knowledge, best practices, and resources.
• Monitor performance and outcomes of models and refine them for optimized performance.
• Communicate complex analytic solutions and insights in a clear and structured manner to business stakeholders.
• Ensure compliance with data governance, privacy, and security policies.

• Bachelor's degree or Master's Degree in Statistics, Mathematics, Data Science, Economics, or a related field.
• A minimum of 2 years of experience in Quantitative Analytics or decision sciences
• Hands on experience with analytics tools and programming languages such as R, Python, SAS, and SQL.
• Proficiency in machine learning techniques and algorithms, including but not limited to k-NN, Naive Bayes, SVM, and Decision Forests.
• Solid understanding of cloud computing environments and experience with deploying models in cloud environments such as AWS, Azure, or GCP.
• Experience of one or more AI/ML platforms in cloud such as Sagemaker, Dataiku, DataRobot, H2O.ai, Snowpark, ModelOp Center, and Domino Data Lab.
• Strong knowledge of statistical modeling, machine learning algorithms, and data analysis techniques.
• Proficiency in utilizing a range of Machine/Deep Learning algorithms and frameworks, including TensorFlow, PyTorch, scikit-learn, Spark ML, Torch, Huggingface, Keras, Caffe, and CNTK.
• Experience in following waterfall, iterative, scaled agile, scrum, and kanban methodologies.
• Hands-on experience with On-prem & cloud data platforms such as Snowflake, AWS Redshift, Azure Synapse Analytics, Databricks, AWS Aurora, Oracle Exadata, SQL server, Hadoop, Spark, SAS and R.
• Experience with Graph Networks, Fraud detection, financial crimes implementations
• Proficiency in deep learning algorithms such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Radial Basis Function Networks (RBFNs), Multilayer Perceptron (MLPs), Self-Organizing Maps (SOMs).
• Implemented machine learning models by leveraging algorithms such as Linear Regression, Decision tree, SVM, Clustering, Naive Bayes, KNN, Random Forest, PCA, AdaBoost.
• Expertise in validation of AI/ML models using one or more methods such as A/B testing, Chi-Square tests, ANOVA, ANCOVA, MANCOVA, MANOVA, Null Hypothesis, Alternate Hypothesis
• Excellent data analysis, profiling and statistics skills coupled with proficiency in SQL tools and technologies such as Oracle, SQL Server, MySQL, Pandas, NumPy, Ggplot, Shiny, SciPy, Sci-Kit Learn, and Matplotlib.
• Demonstrated experience in Hadoop, EMR (Elastic MapReduce), EKS (Elastic Kubernetes Service), ECS (Elastic Container Service), Docker, Kubernetes, and Amazon Sagemaker for visionary, scalable, and efficient machine learning workflows.
• Experience in handling high volume of data in structure, semi-structured and unstructured formats such as relational, flat files, XML, JSON, Parquet, Avro, Mainframe copybooks, CSV, Fixed with and hierarchy files.
• Knowledge of big data technologies such as Hadoop, Spark, or similar.
• Excellent analytical and problem-solving skills.
• Ability to communicate and collaborate with diverse set of team members.

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