Machine Learning Engineer / 100% remote
We are seeking a Machine Learning Engineer to join a leading organization driving innovation in large-scale data solutions. This role focuses on designing and deploying sophisticated data models to optimize operational and business strategies. The ideal candidate has extensive experience in data science, a deep understanding of analytics frameworks, and a background in high-performing environments such as FAANG companies.
This position requires strong technical expertise, the ability to solve complex problems at scale, and the drive to deliver impactful insights that shape the future of data-driven decision-making.
Required Skills & Experience:
Bachelors or Masters degree in Data Science, Computer Science, or related field.
At least 8 years of experience in data science, with expertise in statistical modeling, machine learning, and predictive analytics.
Proficiency in Python, R, or Scala for data modeling and algorithm development.
Strong knowledge of distributed computing frameworks such as Apache Spark or Hadoop.
Experience with cloud platforms like AWS, Google Cloud, or Azure for data storage and processing.
Expertise in building scalable data pipelines and real-time processing systems.
Familiarity with CI/CD pipelines for deploying machine learning models into production environments.
Exceptional problem-solving skills and the ability to translate complex data challenges into actionable solutions.
Desired Skills & Experience:
Experience with A/B testing, experimental design, and evaluating business impact through data-driven approaches.
Knowledge of data governance, compliance, and security best practices.
Strong background in optimizing models for scalability and performance in cloud-based ecosystems.
Prior experience working in high-growth technology environments or with large-scale datasets.
What You Will Be Doing:
Tech Breakdown:
50% Data Science Model Development
30% Data Engineering for Model Integration
20% Collaboration with Product and Engineering Teams
Daily Responsibilities: 60% Hands-On Development and Optimization of Models 20% Model Deployment and Monitoring 20% Cross-Functional Collaboration and Reporting
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