Required Skills & Experience
- Proficiency in Python, R, and SQL for data manipulation and analysis.
- Experience with big data technologies such as Apache Spark, Hadoop, Hive, and cloud data platforms.
- Familiarity with machine learning frameworks like TensorFlow, PyTorch, and scikit-learn.
- Knowledge of natural language processing techniques and tools.
- Experience with data visualization tools such as Tableau.
- Understanding of MLOps, CI/CD pipelines, and container orchestration tools like Kubernetes and Docker.
- Strong statistical modeling and predictive analytics skills.
Soft Skills:
- Excellent problem-solving and analytical abilities.
- Strong communication skills to explain complex models in clear, non-technical terms.
- Collaborative mindset to work effectively in cross-functional teams.
- Curiosity and a continuous learning attitude to keep pace with evolving AI/ML technologies.
- Project management skills to handle multiple tasks and deadlines efficiently.
Nice to Have Skills & Experience
Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
- Minimum of 7 years of professional experience in data science or related roles.
- Proven track record of deploying machine learning models in production environments.
- Experience with responsible AI principles and data governance is a plus.
Job Description
We are seeking an experienced Data Scientist to develop and deploy end-to-end machine learning models that drive actionable business insights. The ideal candidate will build scalable ML pipelines, implement real-time ML models, and integrate these models into production environments. This role requires continuous monitoring and retraining of models to ensure optimal performance and data quality.
Key Responsibilities:
- Develop, test, and deploy machine learning models for predictive analytics and decision support.
- Build scalable and efficient ML pipelines to process large datasets.
- Implement real-time machine learning models and integrate them into production systems.
- Continuously monitor model performance and retrain models as needed.
- Process, clean, and improve data quality for model training and analysis.
- Conduct statistical analysis, feature engineering, and data mining to extract insights.
- Create data visualizations and communicate findings effectively to stakeholders.
- Collaborate with software engineers, data engineers, and IT teams for seamless integration and deployment.
- Stay current with advancements in machine learning frameworks, tools, and best practices.
- Ensure compliance with data security, privacy, and responsible AI/ML practices.