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