Data Scientist
Summary
Data Scientist (mid-level, ~4 years) who owns ML/AI models end to end: framing business problems, preparing data, training and fine-tuning models (including LLMs/transformers), and deploying and monitoring them in production. Core stack: Python, pandas/NumPy, scikit-learn, PyTorch/TensorFlow, SQL and MLOps tools like MLflow.
Job Description – Data Scientist (4 Years)
Role overview
We are hiring a Data Scientist with about 4 years of experience to design, write, train and ship AI/ML models that solve real business problems. You will own models end to end: framing the problem, preparing data, building and fine-tuning models, evaluating them, and working with engineers to put them into production.
Detail | Value |
Title | Data Scientist |
Experience | 4 years (3.5–6 considered) |
Company | Elfonze |
Location | To be confirmed (on-site / hybrid / remote) |
Employment type | Full-time |
Reports to | To be confirmed (e.g., Lead Data Scientist / Head of AI) |
Key responsibilities
Work with business and product teams to turn problems into well-defined ML tasks with clear success metrics.
Collect, clean, label and engineer features from structured and unstructured data (text, images, logs, tabular).
Write and train machine learning and deep learning models from scratch, and fine-tune pre-trained models (including LLMs and transformer models) for specific use cases.
Design experiments, tune hyperparameters, and track runs to compare model versions reproducibly.
Evaluate models with the right metrics (e.g., precision/recall, F1, AUC, RMSE, BLEU/ROUGE) and test for bias, drift and robustness.
Optimise models for inference speed, cost and memory (quantisation, distillation, batching).
Package and deploy models with ML engineers via APIs, batch pipelines or cloud services, and monitor them in production.
Build and maintain training and data pipelines that are versioned, tested and documented.
Explain results to non-technical stakeholders through clear reports and dashboards.
Mentor junior team members and contribute to code reviews and best practices.
Required qualifications and skills
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics or a related field.
About 4 years of hands-on experience building and training ML/AI models, with at least 2 models taken to production.
Strong Python, plus pandas, NumPy and scikit-learn.
Deep learning experience with PyTorch or TensorFlow/Keras, including writing custom training loops and model architectures.
Working knowledge of NLP or computer vision; experience with transformers (e.g., Hugging Face) and fine-tuning LLMs.
Solid grounding in statistics, probability, linear algebra and optimisation.
SQL and experience working with large datasets.
Experience with experiment tracking and MLOps tools (e.g., MLflow, Weights & Biases, DVC) and Git.
Experience training on GPUs in at least one cloud (AWS SageMaker, Azure ML or GCP Vertex AI).
Clear written and verbal communication.
Preferred skills