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Data Scientist

Rulesiq -
Remote

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Job details

Full-time

Qualifications

TensorFlow
CI/CD
Cloud infrastructure
Law
Computer Science
Kubernetes
Big data
Software deployment
Spark
NumPy
System design
MongoDB
Master's degree
Pandas
AWS
Analysis skills
Docker
Bachelor's degree
Machine learning
Continuous integration
Deep learning
Natural language processing
APIs
Predictive analytics
S3
Data science
Clustering
Business requirements
Python
Data Science
Hadoop

Full job description

Job Description:

Position Title: Data Scientist/Machine Learning Engineer

Experience: - Minimum 3 Years
Location: Remote
Employment Type: Full-Time with Rulesiq

Role Summary
We are seeking a highly skilled and versatile Data Scientist/Machine Learning Engineer to join our team. The ideal candidate will have a strong foundation in machine learning, data science, and software engineering, coupled with the ability to design and implement end-to-end systems. This role involves working with cutting-edge technologies, including LLMs, recommendation models, and NLP, while leveraging big data engineering, system design, and cloud infrastructure to deliver impactful solutions.

Key Responsibilities

Machine Learning & Data Science
- Develop and deploy machine learning models for various use cases such as recommendation systems, propensity scoring, and NLP.
- Design, train, and fine-tune large language models (LLMs) and integrate them into production workflows.
- Conduct exploratory data analysis (EDA), feature engineering, and statistical modeling to derive actionable insights.
Big Data Engineering
- Build and optimize data pipelines and workflows for large-scale data processing using tools like Apache Spark, EMR, or similar.
- Collaborate with the data engineering team to ensure data integrity, scalability, and efficiency.
System Design & Development
- Architect and implement end-to-end ML systems, from data ingestion to model deployment and monitoring.
- Develop robust and scalable APIs for model integration and data access.
- Ensure seamless integration with backend systems (MongoDB) and cloud infrastructure (AWS).
Infrastructure & DevOps
- Containerize applications and ML models using Docker, ensuring portability and consistency across environments.
- Orchestrate and manage deployments using Kubernetes.
- Monitor and optimize system performance, ensuring high availability and reliability.
Cloud Computing & Database Management
- Utilize AWS services such as S3, Lambda, SageMaker, and ECS for building and deploying solutions.
- Design efficient and scalable data storage solutions using MongoDB and related tools.
Collaboration & Communication
- Work closely with cross-functional teams, including data engineers, software developers, and product managers.
- Translate business requirements into technical solutions.

Requirements

Technical Skills

Proficiency in Python, with expertise in libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, and Hugging Face Transformers.
Strong understanding of machine learning algorithms, deep learning architectures, and NLP techniques.
Hands-on experience with recommendation systems, propensity scoring, and statistical methods.
Knowledge of big data tools (e.g., Spark, Hadoop) and stream processing.
Solid experience with API development and integration.
Expertise in Docker, Kubernetes, and CI/CD practices.
Familiarity with AWS services and cloud-native architectures.

Analytical & Design Skills

Strong grasp of data science concepts, including predictive modeling, clustering, and classification.
Experience with LLM fine-tuning and deployment for NLP applications.
Sound understanding of system design principles and infrastructure best practices.

Education & Experience

Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
3+ (3-5)years of professional experience in machine learning engineering or data science roles.
Previous experience in building and deploying end-to-end ML pipelines in production environments.

Nice-to-Have Skills

Experience with MongoDB Atlas and serverless architectures.
Knowledge of MLOps tools and practices for productionizing ML models.
Familiarity with monitoring and observability tools (e.g., Prometheus, Grafana).

Quick apply

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