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Data Scientist

Data Scientist

True Tech Professionals

0 years

0 Lacs

gurugram haryana india

Posted:1 day ago| Platform:

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Skills Required

data ml learning development design deployment personalization drive model query research prototype engineering tuning quantization retrieval latency reliability strategy training consistency evaluation numpy software pytorch algorithms ranking aws gcp azure mlflow docker

Work Mode

On-site

Job Type

Full Time

Job Description

Applied Scientist / ML Engineer (Search & Recommendations)

Applied Scientist / Machine Learning Engineer

Key Responsibilities

Search & Recommendation Development

Lead the end-to-end design, development, and deployment of search, personalization, and recommendation algorithms.
Build systems that significantly enhance user experience and drive measurable business impact.

Transformer-Based Model Implementation

Apply, fine-tune, and optimize models such as BERT, RoBERTa, and other encoder architectures for:
Semantic search
Relevance ranking
Query understanding
Embedding generation

Large Language Model (LLM) Innovation

Research, prototype, and implement solutions using LLMs.
Work on model selection, prompt engineering, LoRA-based fine-tuning, and quantization for efficient inference.
Design and implement
RAG (Retrieval-Augmented Generation)
systems using vector databases and advanced retrieval pipelines.

ML Productionization (MLOps)

Build, train, validate, and deploy machine learning models into scalable, low-latency production environments.
Collaborate with engineering teams to ensure reliability, robustness, and maintainability.

Data Strategy & Feature Engineering

Partner with Data Engineering to define datasets and develop innovative features for training and evaluation.
Ensure data quality and consistency across search and recommendation pipelines.

Evaluation & Optimization

Define and track KPIs such as NDCG, CTR, latency, perplexity, and other model metrics.
Continuously iterate to improve model performance and system quality.

Essential Technical Qualifications

MS/PhD
in Computer Science, Data Science, Engineering, or equivalent experience.
Expert-level Python
skills; strong knowledge of ML/DL libraries (NumPy, Pandas, etc.) and solid software engineering practices.
Deep experience with
PyTorch or TensorFlow
.
Proven hands-on work with
Transformer models
(BERT, encoder-only models) for IR, NLU, or embedding generation.
Practical experience with
LLMs
, including fine-tuning, deployment, and familiarity with frameworks such as Hugging Face, LangChain, and LlamaIndex.
Strong foundational understanding of classical ML algorithms and statistical modeling.
Direct experience building or optimizing
search ranking systems
, recommendation engines, dense retrieval, or vector-based search.
Experience with
cloud platforms
(AWS, GCP, Azure) and
MLOps tools
such as MLFlow, Kubeflow, Docker, Kubernetes.

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