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Enterprise Minds, Inc
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Data Scientist

Data Scientist

Enterprise Minds, Inc

8 years

0 Lacs

india

Posted:1 week ago| Platform:

Apply

Skills Required

data design learning research model reliability devops ai ml development efficiency pipeline training evaluation consistency versioning monitoring mlflow aws azure docker drift latency collaboration agile planning code engineering coding tensorflow deployment spark kafka sql nosql tableau power matplotlib communication software service tooling api inference

Work Mode

Remote

Job Type

Full Time

Job Description

Location:

Notice Period:

Experience:

Role Summary

Data Scientist / Data Science Engineer

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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