About this role
**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.