Job Description

About the Role

We are seeking a motivated and technically skilled

Data Scientist

with 4+ years of experience in applying AI/ML techniques to solve real-world problems. The ideal candidate will possess a solid foundation in machine learning, statistical modeling, and data analysis, with hands-on experience working with large datasets and deploying models in production environments.

Key Responsibilities:

Design, develop, and deploy

machine learning models

and

AI solutions

for business problems across multiple domains. Collaborate with cross-functional teams including engineering, product, and analytics to identify opportunities for data-driven
solutions.
Perform

exploratory data analysis (EDA),

feature engineering

, and

model selection

using industry best practices. Conduct

experiments

and perform

model validation

and

performance tuning

. Build reusable code and libraries for future use and contribute to data science platform improvements. Present findings and insights clearly to both technical and non-technical stakeholders.

Required Skills & Qualifications:

4+

years of relevant experience in data science, with hands-on experience in

machine learning, deep learning,

or

AI systems

. Proficiency in

Python

and relevant libraries (e.g., Pandas, Scikit-learn, TensorFlow, PyTorch, NumPy, etc.). Strong understanding of

supervised, unsupervised

, and

reinforcement learning

techniques. Experience with

SQL, data wrangling

, and working with large datasets. Exposure to cloud platforms like

AWS, Azure

, or

GCP

is a plus. Familiarity with

MLOps, model deployment,

and

version control

(e.g., Git).

Preferred Qualifications:

Experience working with

NLP, computer vision,

or

time-series forecasting.

Familiarity with

data visualization tools

(e.g., Matplotlib, Seaborn, Power BI, Tableau). Understanding of

A/B testing, causal inference,

and

statistical hypothesis testing.

Ability to work in a

fast-paced, agile environment

with minimal supervision. Experience in GenAI frameworks

(OpenAI, Vertex AI), multi-agent architectures, and MLOps tooling.

Tech Stack:

Languages & Frameworks:

Python, Java, SQL, JavaScript/TypeScript TensorFlow, PyTorch, Scikit-learn

Cloud Platform:

GCP - Vertex AI, BigQuery, Cloud Functions, Cloud Run, Dataflow, Pub/Sub

Data & MLOps:

BigQuery, AlloyDB, MongoDB, Redis Any relation Database - Oracle , MySQL, MSSQL Any of the MLOps - MLflow, Kubeflow, Airflow Docker, Kubernetes

GenAI & Agents:

Vertex AI Gemini, OpenAI, Claude LangChain, LlamaIndex, FAISS, Pinecone RAG pipelines, autonomous agents, multi-agent orchestration, memory modules, tool use, prompt chaining

Dev Tools:

* GitHub Copilot, ChatGPT, Cursor AI or any other Gen AI Tools