Junior Data Scientist

Job Title: Junior Data Scientist – Deep Learning, Machine Learning & Self-Hosted LLMs

About the Role

• Technically strong and motivated Data Scientist with a foundation in Deep Learning, Machine Learning, Computer Vision, and Large Language Models (LLMs).
• Comfortable developing, evaluating, and deploying machine learning models.
• Experience working with open-weight LLMs and building reliable AI solutions.
• Opportunity to work on practical AI applications, including self-hosted language models, computer vision systems, edge AI deployment, model evaluation, and machine learning engineering.
• Will work with modern AI frameworks and data tools to build solutions that are reproducible, measurable, and suitable for production or stable proof-of-concept environments.

Key Responsibilities

Machine Learning and Deep Learning Development

• Develop, train, fine-tune, and evaluate supervised machine learning and deep learning models.
• Work with PyTorch, Hugging Face Transformers, scikit-learn, and gradient-boosting frameworks.
• Prepare datasets, engineer features, conduct exploratory data analysis, and identify data quality issues.
• Build reproducible training pipelines with appropriate configuration, experiment tracking, and version control.

LLM Fine-Tuning and Self-Hosted AI Solutions

• Fine-tune open-weight Large Language Models and embedding models using techniques such as LoRA and QLoRA.
• Work with Hugging Face PEFT/TRL and relevant fine-tuning tools.
• Support the deployment and evaluation of self-hosted models through internal APIs or inference endpoints.
• Develop LLM-powered applications using retrieval-augmented generation (RAG), prompt engineering, and embedding-based search.
• Evaluate whether fine-tuning, retrieval, or prompt optimization is the most suitable approach for a given business problem.

Model Evaluation, Calibration, and Monitoring

• Design evaluation datasets, gold-standard test sets, and appropriate training and validation splits.
• Identify data leakage, class imbalance, and other factors that affect model performance.
• Evaluate models using metrics such as PR-AUC, precision, recall, Brier score, Expected Calibration Error (ECE), and reliability diagrams.
• Calibrate model probabilities and select decision thresholds based on agreed business requirements.
• Monitor model performance and data drift, document known limitations, and prepare model cards.

Computer Vision and Edge AI

• Develop and evaluate computer vision models for object detection, image segmentation, and optical character recognition (OCR).
• Work with architectures and tools such as YOLO, RT-DETR, SAM, Vision Transformers (ViT), OpenCV, and torchvision.
• Build data annotation, preprocessing, and augmentation pipelines.
• Measure model performance using metrics such as mean Average Precision (mAP) and Intersection over Union (IoU).
• Support model optimization and deployment on edge devices, evaluating accuracy, inference latency, and resource consumption.

Data Engineering and Analytics

• Process structured and unstructured data using Python, pandas, Polars, and DuckDB.
• Write SQL queries and build feature tables for machine learning applications.
• Work with Parquet, Apache Arrow, and dbt for efficient data processing and transformation.
• Handle nested documents and complex data structures while maintaining data quality and reproducibility.

LLM Application Development and Retrieval Systems

• Develop LLM-based applications using frameworks such as LangChain, LangGraph, or LlamaIndex.
• Integrate vector databases such as FAISS, pgvector, Qdrant, or Milvus.
• Evaluate retrieval and generation quality using tools such as Ragas or DeepEval.
• Understand hosted LLM APIs, structured outputs, prompt caching, and batch processing.

Model Deployment and MLOps

• Write modular, readable, typed, and testable Python code following good software engineering practices.
• Develop and test model-serving APIs using FastAPI.
• Use Git, Docker, Linux, Bash, and CI/CD workflows to support model development and deployment.
• Track experiments using MLflow or Weights & Biases and maintain versioned datasets and model artifacts.
• Write unit tests, maintain configurations, and support reproducible model training and deployment.

Requirements & Tech Stack

Programming and Data Science

• Python and SQL.
• PyTorch and scikit-learn.
• Hugging Face Transformers, Datasets, and Accelerate.
• pandas, Polars, DuckDB, and experience with structured and unstructured data.
• XGBoost, LightGBM, or CatBoost.

Deep Learning and LLMs

• Fundamentals of neural networks, CNNs, and transformer architectures.
• Understanding of LLMs, embeddings, RAG, and prompt engineering.
• Hands-on exposure to fine-tuning techniques such as LoRA or QLoRA.
• Familiarity with Hugging Face PEFT/TRL or equivalent tools.

Computer Vision

• Understanding of object detection, segmentation, and OCR.
• Familiarity with OpenCV, torchvision, Ultralytics, or similar frameworks.
• Knowledge of image preprocessing, augmentation, and model evaluation.

Model Evaluation

• Understanding of classification metrics, precision-recall trade-offs, class imbalance, and data leakage.
• Familiarity with probability calibration, threshold selection, and model monitoring.
• Ability to interpret model performance and communicate limitations clearly.

Engineering and Development

• Git and basic software development workflows.
• Testing, debugging, type hints, and modular Python development.
• Familiarity with Docker, Linux/Bash, REST APIs, and FastAPI.
• Exposure to experiment tracking and reproducible training workflows.

Qualifications

• 0–1 year of applied Machine Learning experience, with practical exposure to building, evaluating, or deploying ML models.
• Experience with at least one supervised machine learning project where model predictions or calibrated probabilities supported a human decision.
• Academic projects, internships, research work, open-source contributions, or independently developed projects may be considered.
• A strong understanding of model evaluation, data preprocessing, and fundamental machine learning concepts.
• Bachelor's or Master's degree in Computer Science, Data Science, Electrical Engineering, Electronics Engineering, Artificial Intelligence, or a related technical discipline.
• Candidates with equivalent practical experience, strong projects, or demonstrated capabilities in Computer Vision and Edge AI may also be considered.
• A Master's degree or PhD is an advantage but is not mandatory.