Lead Data Scientist, CI&T

• Bachelor’s Degree in Computer Science/Engineering, Applied Math, Statistics, Physics or other related quantitative areas
• Advanced oral and written communication skills in English
• Ability to understand mathematical models and algorithms in research papers, and to implement them into running software for Proof-of-Concepts and projects
• Ability to explore big data without a specific problem defined, in order to come up with the right questions and provide interesting findings
• Ability to provide visibility of the progress of tasks to the team by means of small deliverables
• Proficient in computer languages like Python or R, and SQL, making use of the best frameworks for machine learning pipelines, data visualization, manipulation and transforming, models training and evaluation, and models deployment
• Experience with common feature engineering techniques and machine learning algorithms for Supervised and Unsupervised Learning, like Regression, Classification, Clustering, Dimensionality Reduction, Association Rules, Ranking and Recommender Systems
• Experience with Natural Language Processing (NLP and NLU)
• Experience using Generative AI systems (e.g. ChatGPT) and best practices (e.g. Prompt Engineering)
• Understanding the key concepts on how to apply Generative AI in building RAG solutions (embeddings, dense search)
• Business sense and consulting behavior to identify and breakdown problems, define and evaluate hypotheses
• Think critically and act in a detail-oriented fashion while keeping the "big picture" in mind
• Ability to provide creative and innovative approaches to problem solving
• Ability to work independently and within a collaborative team environment

Desirable

• We are looking for scientists who are passionate about data and are eager to tackle big challenges using Data Science and Machine Learning
• The main focus of this role is to solve non-trivial business problems in Fortune 500 companies
• This person will mostly work with a mix of structured and unstructured, and use scientific methods and state-of-the-art techniques and tools to help our customers achieve their business objectives
• Understand complex business problems and translate them into structured data problems
• Capture and explore complex data sets (structured and unstructured data)
• Prototype Model of different complexity (business analysis, statistical models, machine learning) using modern data science tools (Notebooks, Clouds)
• Design and implementation of machine learning models, metrics and application of feature engineering techniques applied to customers problems
• Support pre-sales in business opportunities and the engineering teams in the implementation of production-ready solutions involving machine learning
• Evaluate hypotheses and the impact of machine learning algorithms on key business metrics. Simulations and offline/online experimentation (via A/B tests) is part of the game
• Research and understand user behavior patterns, such as user engagement and segmentation, using machine learning models, to help test hypotheses
• Communicate findings effectively to an audience of engineers and executives