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.
