Grazitti Interactive - Data Scientist

Key Responsibilities Solving & Strategy

• Collaborate with stakeholders across business units (e.g., Product, Marketing, Operations) to identify key business questions and formulate data-driven solutions.
• Translate business needs into a clear, structured data science problem and define the project scope, objectives, and success metrics.
• Drive strategic initiatives by providing data-backed recommendations to senior Analysis & Modeling :
• Perform exploratory data analysis (EDA), data cleaning, and feature engineering to prepare data for modeling.
• Develop, implement, and validate machine learning models (e.g., classification, regression, clustering, time series analysis) to solve complex business problems.
• Design and conduct experiments (A/B testing) to measure the impact of new features or AI & Advanced Techniques :
• Research, evaluate, and apply advanced deep learning techniques, including Generative AI models (e.g., Large Language Models, Diffusion Models), to create innovative solutions.
• Develop and implement strategies for prompt engineering, Retrieval Augmented Generation (RAG), and fine-tuning models to enhance performance and contextual relevance.
• Stay current with the latest research and trends in AI and machine learning, and assess their applicability to our business & Collaboration :
• Clearly communicate findings, methodologies, and the impact of models to both technical and non-technical audiences through reports, dashboards, and presentations.
• Partner with Data Engineers to build scalable data pipelines and with MLOps Engineers to deploy models into production.
• Mentor junior data scientists and contribute to a culture of continuous learning and data literacy within the & Tooling :
• Write production-quality code in Python for data analysis, modeling, and automation.
• Utilize version control systems (e.g., Git) and collaborate effectively on shared codebases.
• Proficiency with relevant data science tools and platforms (e.g., SQL, cloud platforms like AWS, Azure, Qualifications : : Master's or Ph.D. in a quantitative field such as Computer Science, Statistics, Mathematics, Physics, or a related : 5-7 years of hands-on experience as a Data Scientist or in a similar role, with a strong portfolio of projects that have been successfully deployed and delivered measurable business Skills : Expert proficiency in Python and its data science ecosystem (Pandas, NumPy, SciPy, Learning : Deep understanding of a wide range of ML algorithms and statistical modeling Learning : Hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and an understanding of neural network AI : Practical experience with Large Language Models (LLMs), prompt engineering, and fine-tuning. Familiarity with other generative models is a Querying : Strong SQL skills are a : Proven ability to define and solve complex, ambiguous problems with an analytical and methodical : Excellent written and verbal communication skills, with the ability to present complex technical concepts clearly and concisely to diverse Qualifications (Bonus Points) :
• Experience with MLOps principles and tools (e.g., MLflow, Kubeflow).
• Proficiency with cloud-based data warehouses and services (e.g., Snowflake, BigQuery, Redshift, S3).
• Experience with distributed computing frameworks (e.g., Spark, Dask).
• Experience in [specific industry, e.g., FinTech, Healthcare, E-commerce, SaaS].
• Publications in peer-reviewed journals or major conferences.