● Problem Analysis and Project Management Guide and inspire the organization about the business potential and strategy of artificial intelligence Identify data-driven/ML business opportunities Collaborate across the business to understand IT and business constraints Prioritize, scope, and manage data science projects and the corresponding key performance indicators (KPIs) for success Communicate governance principles ● Data Collection and Integration Understand new data sources and process pipelines and catalog/document them Acquire access to various databases and other sources systems such as Oracle, Columnar (Vertica) databases, AWS Data Lake, etc., Create data pipelines for more efficient and repeatable data science projects ● Data Exploration and Preparation Apply statistical analysis and visualization techniques to various data, such as hierarchical clustering, T-distributed Stochastic Neighbor Embedding (t-SNE), principal components analysis (PCA)Machine Learning Generate hypotheses about the underlying mechanics of the business process Test hypotheses using various quantitative methods Display drive and curiosity to understand the business process to its core Network with domain experts to better understand the business mechanics that generated the data ● Machine Learning Apply various ML and advanced analytics techniques to perform classification or prediction tasks Integrate domain knowledge into the ML solution; for example, from an understanding of financial risk, customer journey, quality prediction, sales, marketing Testing of ML models, such as cross-validation, A/B testing, bias and fairness ● Implementation Collaborate with Ops, data engineers, and architects to evaluate and implement ML deployment options Integrate model performance management tools into the current business infrastructure Implement champion/challenger test (A/B tests) on production systems Continuously monitor execution and health of production ML models Establish best practices around ML production infrastructure
• Bachelor's degree in Information Systems, Information Technology, Computer Science or Masters in Mathematics or Statistics • Minimum 2 years of IT experience in working on Data Science projects • Experience to work with team members in multiple geographies and across time zones • Work with stakeholders in change management of digital transformation • Effective verbal and written communication skills within IT and with business clients. • Work effectively with technical peers and business partners • Ability to work independently & provide innovative solutions • They must demonstrate the ability to work in diverse, cross-functional teams in a dynamic business environment. • Candidates should be confident, energetic self-starters, with strong communication skills. • Candidates should exhibit superior presentation skills, including storytelling and other techniques
Coding knowledge and experience in Python or R is must with key packages for ML/AI. Nice to have experience in Angular (Web framework), Jupyter notebook, Google Collab, Flask etc. Experience in one or more of the following commercial/open-source data discovery/analysis platforms: Tableau, PowerBI, Einstein, SAS Enterprise Miner (SAS EM) and/or SAS Visual Data Mining and Machine Learning, Amazon SageMaker and Google Cloud ML. Experience with popular database programming languages including SQL, PL/SQL, for relational databases and Analytics databases such as Vertica, Redshift etc. Experience of working across multiple deployment environments including [cloud, on-premises and hybrid], multiple operating systems and through containerization techniques such as Docker, Kubernetes, AWS Elastic Container Service, and others. Expertise in solving classification and prediction problems e.g vision, text analytics, credit scoring, failure prediction, propensity to buy etc. Knowledge and experience in statistical and data mining techniques: generalized linear model (GLM)/regression, random forest, boosting, trees, text mining, hierarchical clustering, deep learning, convolutional neural network (CNN), recurrent neural network (RNN), T-distributed Stochastic Neighbor Embedding (t-SNE), graph analysis, etc.
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