Role – Data Scientist (Entry Level)
Location – Deerfield, IL
Job Type – Full Time
As a Data Scientist Trainee (AI & GenAI), associate will collaborate with senior data scientists and AI engineers to design and implement advanced analytics and AI-driven solutions. This role provides hands-on exposure to cutting-edge technologies in machine learning, deep learning, and generative AI, enabling associate to transform complex data into intelligent insights and innovative applications. Associate will gain experience in building AI models, fine-tuning large language models (LLMs), and leveraging GenAI tools to solve real-world business challenges in a dynamic retail environment.
Key Responsibilities
• Data Preparation for AI Models: Collect, clean, and preprocess structured and unstructured data for training and evaluation of AI models.
• Model Development & Fine-Tuning: Assist in building and fine-tuning machine learning and deep learning models, including LLMs and generative AI architectures.
• Prompt Engineering & Optimization: Experiment with prompt design and optimization techniques to improve GenAI outputs for business use cases.
• AI-Powered Analytics: Apply AI algorithms for predictive analytics, personalization, and recommendation systems.
• Prototype Development: Support the creation of AI-driven prototypes and proof-of-concepts for automation and customer experience enhancement.
• Visualization & Communication: Develop dashboards and visualizations to present AI-driven insights to stakeholders.
• Research & Innovation: Stay updated on emerging AI trends, frameworks, and tools and contribute ideas for innovative applications.
Qualification and Specialization:
Education
• Master’s degree in Data Science, Computer Science, AI/ML, Statistics, or a closely related field.
• Coursework or thesis/projects in machine learning, deep learning, NLP/LLMs, probability & statistics, and optimization.
Technical Skills (AI & GenAI Focus)
• Programming: Proficiency in Python (pandas, NumPy, scikit-learn); familiarity with SQL for data extraction and transformation.
• Deep Learning: Hands-on exposure to PyTorch or TensorFlow; ability to build and train neural networks (CNNs/RNNs/Transformers).
• LLMs & NLP: Understanding of tokenization, embeddings, attention mechanisms; experience using Hugging Face ecosystems and prompt engineering for LLMs.
• Generative AI: Basic experience with text generation, image generation, or multimodal models; awareness of techniques like fine-tuning, LoRA/PEFT, and RAG (Retrieval-Augmented Generation).
• MLOps Basics: Exposure to model versioning, experiment tracking (e.g., MLflow), and deployment concepts (batch/real-time APIs); understanding of evaluation and monitoring for AI systems.
• Cloud & Data: Familiarity with at least one major cloud platform (Azure, AWS, or GCP) for data/AI services; working knowledge of data pipelines and feature engineering.
Analytics & Research
• Strong foundations in probability, statistical inference, hypothesis testing, and A/B testing.
• Ability to conduct exploratory data analysis (EDA) and communicate findings clearly with visualizations (e.g., matplotlib, seaborn, Plotly/Power BI/Tableau).
Quality, Safety & Ethics (AI)
• Awareness of AI fairness, bias, privacy, and responsible AI practices.
• Familiarity with model evaluation metrics for classification/regression and LLM-specific metrics (quality, hallucination checks, factuality).
Soft Skills
• Curiosity and learning mindset: eagerness to explore new AI frameworks and stay current with advancements.
• Collaboration & communication: ability to translate technical insights for non-technical stakeholders.
• Problem-solving: structured approach to framing problems, building prototypes, and iterating quickly.
Nice-to-Have (Preferred)
• Internship or academic project experience applying LLMs/GenAI to real use cases (e.g., RAG chatbot, summarization, recommendations).
• Contributions to open-source (e.g., Hugging Face, PyTorch) or a personal portfolio (GitHub/Kaggle).
• Certifications (e.g., Azure AI Engineer Associate, AWS Machine Learning Specialty, Google Professional ML Engineer) or Databricks Lakehouse badges.
Unique Experience from this Role:
– Hands-on exposure to cutting-edge AI technologies including Large Language Models (LLMs), Generative AI architectures, and advanced machine learning frameworks.
– Opportunity to fine-tune and optimize LLMs for real-world business applications such as personalization, recommendation systems, and conversational AI.
– Experience in prompt engineering and optimization techniques to enhance GenAI outputs for enterprise use cases.
– Work on end-to-end AI solution development, from data preparation to model deployment, gaining practical knowledge of MLOps and cloud-based AI services.
– Collaborate with industry experts and cross-functional teams to design innovative AI-driven prototypes and proof-of-concepts.
– Exposure to responsible AI practices, including bias detection, ethical AI principles, and compliance with data privacy standards.
– Learn to translate complex AI insights into actionable business strategies, bridging the gap between technology and decision-making.
Learning outcomes for the Trainee:
– Applied ML & DL Foundations: Gain hands-on proficiency in building, training, and evaluating machine learning and deep learning models (including Transformers) for real business use cases.
– LLMs & GenAI Mastery: Learn how to fine tune Large Language Models (LLMs), design effective prompts, and implement GenAI solutions (e.g., RAG pipelines, summarization, personalization).
– Data Engineering for AI: Develop skills in data ingestion, cleansing, feature engineering, and handling unstructured data (text, images) for robust AI model pipelines.
– MLOps Basics: Understand experiment tracking, versioning, CI/CD for models, and monitoring in production to ensure reliability and performance.
– Evaluation & Responsible AI: Apply rigorous evaluation metrics (classification/regression/LLM quality) and practice fairness, bias detection, privacy, and compliance.
– Cloud & Tooling Proficiency: Gain working knowledge of at least one major cloud AI stack (Azure/AWS/GCP), model deployment patterns (batch/real time), and popular frameworks (PyTorch/TensorFlow/Hugging Face).
– Visualization & Storytelling: Learn to translate complex AI insights into clear visuals and business narratives for technical and non technical stakeholders.
– Prototype to Impact: Build end to end AI prototypes and iterate toward viable POCs that improve customer experience or operational efficiency.
– Collaboration & Product Thinking: Work in cross functional teams, convert business problems into AI use cases, and prioritize solutions based on impact and feasibility.
– Continuous Learning Mindset: Cultivate habits for staying current with fast moving AI research, tools, and best practices—and reflecting learnings in project outcomes.
Salary Range – $60K – $70K Per Annum + Benefits