Data Scientist

Elfonze Technologies; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Elfonze Technologies in Bengaluru, India, is hiring a Data Scientist with about 4 years of hands-on experience to design, train and ship AI/ML models that solve real business problems. You will own models end to end—from framing the problem to production deployment—and collaborate with product teams and engineers.

The role requires strong Python, DL frameworks, NLP/CV familiarity, and experience deploying models on cloud platforms.

Qualifications

• 4 years hands-on ML/AI model development.
• At least 2 models in production.
• Strong Python and data libraries.
• Experience with DL frameworks like PyTorch or TensorFlow/Keras.
• Familiarity with NLP or CV and transformers.
• Knowledge of ML Ops and experiment tracking.
• Experience GPU cloud training (AWS/GCP/Azure).
• SQL and large dataset handling.

Responsibilities

• Turn problems into well-defined ML tasks with measurable success.
• Collect, clean, label and engineer features from various data.
• Build and fine-tune ML/DL models, including transformers and LLMs.
• Design experiments, tune hyperparameters, track runs.
• Evaluate models with relevant metrics and check bias, drift, robustness.
• Optimize for inference speed, cost and memory.
• Package and deploy via APIs, pipelines or cloud services.
• Create training/data pipelines that are versioned and tested.
• Explain results to non-technical stakeholders with reports.
• Mentor juniors and participate in code reviews.

Skills

Python programming

Pandas

NumPy

scikit-learn

PyTorch

TensorFlow/Keras

NLP

Computer Vision

Transformers

HuggingFace

LLMs fine-tuning

Statistics

Linear algebra

Optimization

SQL

MLflow

Weights & Biases

DVC

Git

AWS SageMaker

Azure ML

Vertex AI

GPU Training

Education

Bachelor's or Master's in CS/DS/Stats/Math

Related field degree

Tools

MLflow

Weights & Biases

DVC

Git

HuggingFace

SageMaker

Azure ML

Vertex AI

Job description

Job Description – Data Scientist (4 Years)

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.

Title

Data Scientist

Experience

4 years (3.5–6 considered)

Company

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).