Application of Data Learning algorithms and libraries. Evaluating & identifying the right Data Learning and data science techniques & toolsets to address a variety of predictive analytics problems Ability to explain Data learning concepts and findings or techniques to varied stakeholder. Collaborate with the data science team to analyze large datasets, extract meaningful insights, and present findings. Assist in designing, developing, and implementing machine learning models and algorithms. Clean, preprocess, and manipulate data to ensure its quality and suitability for analysis. Support the team in data visualization and creating interactive dashboards to communicate results effectively. Contribute to the development of data-driven solutions to solve business challenges. Stay updated with the latest trends and advancements in data science and machine
Identify valuable data sources and automate collection processes. Undertake preprocessing of structured and unstructured data. Analyze large amounts of information to discover trends and patterns. Build predictive models and machine-learning algorithms. Combine models through ensemble modeling. Present information using data visualization techniques. Propose solutions and strategies to business challenges. Collaborate with engineering and product development teams. Communicate your findings to the appropriate teams through visualizations. Collaborate and communicate findings to diverse stakeholders. Provide solutions but not limited to: Image recognition, natural language processing, Sentiment Analysis, Concept Extraction, Recommender Systems, Clustering, Customer Segmentation, Propensity Modeling, Churn Modeling, Lifetime Value Estimation, Forecasting, Modeling Response to Incentives, Marketing Mix Optimization, Price Optimization. Follow/maintain an agile methodology for delivering on project milestones. Excellent oral, presentation, and written communication skills.
Develop and deploy scalable deep learning models to improve the search experience of Target guests. Architect and implement large-scale AI systems using test-driven development practices. Build NLP technology for query and document analysis, processing, and understanding. Create data pipelines for feature/label extraction and generation. Conduct data analysis to identify opportunities and improve models. Research or industrial experiences in relevant fields, such as information retrieval, machine learning, NLP, or recommendation systems Proficient in Python or Java with Strong coding and problem-solving skills. Solid modeling skills in Python. Good understanding of search indexing, retrieval, and ranking. Extensive experience in implementing and integrating ML models in production with high traffic. Experience querying large databases with SQL, HQL, or some variations. Experience with Semantic Search is a plus. Experience in Tuning LLMs is plus. Proven track record of achieving significant engineering or academic results. Excellent interpersonal and communication skills to work with global teams.