Job Description

Job Summary:

We are looking for a highly skilled and experienced

Data Scientist

to join our growing analytics team. As a Data Scientist, you will be responsible for

extracting insights from large, complex datasets

, building

predictive and prescriptive models

, and helping to

drive data-driven decisions

across the organization. You will work closely with cross-functional teams to design experiments, prototype solutions, and implement algorithms that have a tangible impact on the business.

Key Responsibilities:

Data Exploration & Analysis:

Understand business objectives and translate them into data science problems. Perform exploratory data analysis using statistical techniques and visualization tools. Analyze structured and unstructured data from multiple sources (databases, APIs, flat files, etc.).

Model Development:

Build and validate machine learning and statistical models (classification, regression, clustering, NLP, time-series, etc.). Optimize model performance using feature engineering, hyperparameter tuning, and model selection techniques. Use ML Ops tools for model deployment and monitoring in production environments.

Business & Stakeholder Collaboration:

Collaborate with business stakeholders, analysts, engineers, and product teams to gather requirements and present findings. Translate complex analytical concepts into business-friendly language. Support experimentation and A/B testing frameworks.

Data Engineering Support:

Work with data engineers to define data pipelines and ensure high data quality. Write efficient, reusable, and modular code in Python, SQL, or Spark. Assist in creating and maintaining data lakes, feature stores, or model registries.

Documentation & Reporting:

Maintain detailed documentation of methodologies, models, assumptions, and results. Create dashboards and reports to communicate key insights to stakeholders.

Required Skills & Qualifications:

Bachelor's/Master's/Ph.D. in Computer Science, Statistics, Mathematics, Engineering, or related field. 2-3 years of hands-on experience in a data science or machine learning role. Proficiency in

Python

or

R

, and SQL. Experience with frameworks like

scikit-learn, TensorFlow, PyTorch

, or

XGBoost

. Strong grasp of statistical modeling, hypothesis testing, and machine learning algorithms. Experience with

data wrangling

using tools like Pandas, Spark, or Dask. Exposure to

cloud platforms

like AWS, GCP, or Azure. Familiarity with

version control

(Git), CI/CD, and containerization (Docker) is a plus. Excellent problem-solving and communication skills.

Preferred Qualifications:

Experience with

time-series forecasting

,

natural language processing

, or

computer vision

. Understanding of

big data platforms

(Hadoop, Hive, Presto). Knowledge of

ML Ops frameworks

like MLflow, Kubeflow, or SageMaker. * Prior experience in domains like

retail, finance, healthcare, automobile, or marketing analytics

.