Data Scientist

Job Description: Data Scientist (8+ Years)

Location: Remote

Notice Period: Immediate to 15 Days

Experience: 8+ Years

Role Summary

We are seeking a highly experienced Data Scientist / Data Science Engineer to design, build, deploy, and maintain machine learning models and scalable production systems. This role focuses on bridging the gap between research and production by implementing MLOps best practices, ensuring model reliability, and optimizing performance at scale. You will collaborate with cross-functional teams including Data Scientists, Data Engineers, DevOps, and Product Managers to deliver enterprise-grade AI/ML solutions.

Key Responsibilities

- Model Development & Deployment
- Develop, optimize, and deploy machine learning models into scalable and secure production environments.
- Transition research prototypes into production-grade systems with high efficiency and maintainability.
- Data Pipeline Engineering
- Build and optimize data pipelines for model training, evaluation, and inference.
- Ensure high data quality, availability, and consistency across the ML lifecycle.
- MLOps & Infrastructure
- Implement frameworks for model versioning, monitoring, retraining, and CI/CD.
- Use platforms such as MLflow, Kubeflow, Airflow for end-to-end ML lifecycle management.
- Cloud & Platform Engineering
- Deploy ML solutions using cloud platforms (AWS, Azure, GCP) and their managed ML services.
- Manage containerized workloads using Docker and Kubernetes.
- System Reliability & Performance
- Monitor production systems for data drift, performance degradation, and anomalies.
- Optimize latency, throughput, and cost of ML services in production.
- Collaboration & Agile Delivery
- Work in Agile teams, participate in sprint planning, code reviews, and maintain clear documentation.
- Partner with business and technical stakeholders to align ML solutions with business goals.

Required Qualifications & Skills

- Education: Bachelor's or Master's in Computer Science, Data Science, Engineering, or related field.
- Technical Skills:
- Strong coding expertise in Python.
- Hands-on experience with ML frameworks: scikit-learn, TensorFlow, PyTorch.
- Proficiency in cloud platforms: AWS, Azure, or GCP.
- Strong understanding of MLOps tools & frameworks: MLflow, Kubeflow, Airflow.
- Experience with Docker, Kubernetes for model deployment at scale.
- Familiarity with data engineering tools (Spark, Kafka, SQL/NoSQL databases).
- Competency in data visualization: Tableau, Power BI, matplotlib, seaborn.
- Soft Skills:
- Strong analytical and problem-solving abilities.
- Excellent communication and stakeholder management.
- Adaptable, detail-oriented, and collaborative.
- Familiarity with Agile practices.

Preferred Qualifications

- Experience with real-time or streaming ML systems.
- Implemented CI/CD pipelines for ML models.
- Knowledge of Responsible AI principles (fairness, explainability, bias mitigation).

Key Relationships

- Internal: Data Scientists, Data Engineers, DevOps, Software Engineers, Product Managers, Business Stakeholders.
- External: Cloud Service Providers, Vendors, AI/ML Communities.

Role Dimensions

- Decision-Making Authority: Choice of ML tools, frameworks, deployment strategies.
- Budget Responsibility: Influence on cloud and ML tooling costs.
- Team Size: Individual contributor / small team lead.
- Geographic Scope: Global or regional project scope depending on business needs.

Success Measures (KPIs)

- Number of ML models deployed successfully to production.
- Reduced lead time for ML model deployment.
- Model/API uptime and system reliability.
- Optimized inference latency and cost-efficiency.
- Coverage of automated MLOps pipelines.
- Cross-functional collaboration and stakeholder satisfaction.

Interested candidates can share their CVs to deepika.balijepally@eminds.ai

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