About the Opportunity :

We are seeking a passionate and technically proficient Data Scientist / Machine Learning Engineer to design, build, and deploy intelligent, data-driven solutions that power predictive analytics, automation, and decision-making systems.

The ideal candidate will have strong experience in Python/R, machine learning frameworks (TensorFlow, PyTorch, Scikit-learn), and feature engineering, along with a solid grasp of data pipeline development and model deployment on cloud platforms.

This role combines applied data science with production-grade ML engineering bridging the gap between algorithm development and real-world implementation.

Key Responsibilities :

- Design and implement machine learning models, including regression, classification, clustering, and time-series forecasting.

- Develop and maintain data pipelines for model training, validation, and deployment.

- Perform feature engineering, data cleaning, and transformation on structured and unstructured datasets.

- Work with large-scale datasets using SQL, Pandas, and Spark to enable high-performance model training.

- Collaborate with product and engineering teams to integrate ML models into production systems.

- Optimize model performance using techniques such as hyperparameter tuning, cross-validation, and regularization.

- Utilize deep learning frameworks (TensorFlow, PyTorch) for advanced use cases such as NLP, computer vision, or anomaly detection.

- Implement end-to-end MLOps pipelines for model monitoring, versioning, and retraining.

- Use cloud-based ML platforms (AWS Sagemaker, Azure ML, or GCP Vertex AI) for scalable experimentation and deployment.

- Communicate analytical findings and insights to technical and non-technical stakeholders through clear visualizations and reports.

Required Skills and Qualifications :

- 37 years of experience in data science, applied machine learning, or ML engineering.

- Strong programming skills in Python (preferred) or R, with experience using libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch.

- Hands-on experience with data wrangling, feature extraction, and statistical analysis.

- Good understanding of supervised and unsupervised learning algorithms, and when to apply each.

- Experience in data pipeline development, ETL processes, and integration with data lakes or warehouses.

- Familiarity with SQL and NoSQL databases for data storage and retrieval.

- Exposure to MLOps, model deployment, and CI/CD practices in production environments.

- Strong mathematical foundation in probability, statistics, and linear algebra.

- Excellent problem-solving, critical thinking, and communication skills.

- Bachelors or Masters degree in Computer Science, Data Science, Statistics, or related quantitative fields.

Preferred Skills :

- Experience with natural language processing (NLP) or computer vision projects.

- Familiarity with big data frameworks such as Apache Spark, Databricks, or Hadoop.

- Hands-on experience with data visualization tools like Power BI, Tableau, or Plotly.

- Knowledge of model interpretability techniques (SHAP, LIME, Explainable AI).

- Experience working in Agile / DevOps environments.

- Certifications in AWS Machine Learning, Google Professional ML Engineer, or Microsoft Azure Data Scientist Associate