Data Scientist with AI/ML(Full time role)

Role: Data Scientist (AI/ML Focus)

Objective:
Leverage data science techniques and machine learning (ML) or artificial intelligence (AI) algorithms to extract insights, build predictive models, and deploy intelligent systems that support data-driven decision-making and automation.

Core Responsibilities

1. Data Collection & Preprocessing

• Acquire and integrate data from various sources (databases, APIs, files, IoT, etc.).

• Clean, validate, and preprocess data (handling missing values, normalization, feature engineering).

• Develop and maintain data pipelines using tools like Airflow, Kafka, or Spark.

2. Exploratory Data Analysis (EDA)

• Analyze large, complex datasets to identify patterns, trends, and outliers.

• Visualize data using tools like Matplotlib, Seaborn, Plotly, or Power BI.

• Provide actionable insights to business stakeholders.

3. Model Development

• Design, build, and train ML/AI models for tasks such as classification, regression, clustering, NLP, computer vision, or time series forecasting.

• Select appropriate algorithms (e.g., XGBoost, Random Forest, SVM, CNN, RNN, Transformers).

• Tune hyperparameters and evaluate model performance using metrics like precision, recall, F1-score, AUC, RMSE.

4. AI/ML Research & Innovation

• Stay updated with recent advances in AI/ML and assess applicability to business needs.

• Experiment with deep learning models (using TensorFlow, PyTorch) and foundation models (e.g., GPT, BERT).

• Collaborate on designing AI-based prototypes or proofs of concept.

5. Model Deployment & MLOps

• Package and deploy models into production environments using Flask, FastAPI, Docker, Kubernetes, or cloud platforms (AWS Sagemaker, Azure ML, Google Cloud Platform AI Platform).

• Monitor model performance and retrain/update models as needed.

• Implement CI/CD pipelines and use tools like MLflow or DVC for versioning.

6. Collaboration & Communication

• Work with cross-functional teams including data engineers, analysts, product managers, and software engineers.

• Translate technical findings into business impact for stakeholders.

• Document processes, methodologies, and results clearly.

7. Data Governance & Ethics

• Ensure compliance with data privacy laws (GDPR, HIPAA, etc.).

• Implement ethical AI practices (bias detection, fairness, explainability).

• Manage data access and security responsibly.

Required Skills & Tools

• Programming: Python, R, SQL, Spark

• ML/AI Libraries: Scikit-learn, TensorFlow, PyTorch, XGBoost

• Data Tools: Pandas, NumPy, Jupyter, Apache Spark

• Visualization: Matplotlib, Seaborn, Plotly, Tableau, Power BI

• Deployment: Docker, Flask/FastAPI, REST APIs, AWS/Google Cloud Platform/Azure

• MLOps Tools: MLflow, Kubeflow, Airflow, DVC

• Version Control: Git/GitHub

• Soft Skills: Communication, critical thinking, problem-solving, collaboration