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Key Responsibilities and Required Skills for Data Science Engineer

💰 $110,000 - $170,000

Data ScienceMachine LearningEngineeringMLOpsAnalytics

🎯 Role Definition

The Data Science Engineer (also called ML Engineer or Production Data Scientist) is responsible for turning data and predictive models into reliable, scalable software and services. This role combines advanced machine learning knowledge with software engineering, data engineering, and MLOps practices to design, implement, deploy, monitor, and iterate production-grade ML systems that drive measurable business outcomes.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Scientist transitioning to production ML engineering
  • Software Engineer with machine learning exposure
  • Data Engineer interested in model-driven product features

Advancement To:

  • Senior Data Science Engineer / Staff ML Engineer
  • ML Engineering Lead / Principal Engineer
  • Head of ML / Director of Data Science & ML Operations

Lateral Moves:

  • Machine Learning Engineer
  • Data Engineer
  • Research Scientist
  • Analytics Engineer

Core Responsibilities

Primary Functions

  • Design, build, and maintain scalable, production-grade machine learning pipelines that ingest, clean, transform, and validate data for feature generation and model training, using technologies such as Airflow, Prefect, or Kubeflow.
  • Develop robust feature engineering, feature stores, and transformation logic to ensure reproducible, high-quality inputs for models across training and serving environments.
  • Architect and implement model training workflows that support distributed training, hyperparameter tuning, experiment tracking, and reproducibility using tools like MLflow, Weights & Biases, or TensorBoard.
  • Deploy machine learning models into production as REST/gRPC microservices, batch jobs, or serverless functions with observability and automated rollback strategies.
  • Build and maintain CI/CD pipelines for model code, data validation, and infrastructure-as-code (IaC) to automate testing, packaging, and safe deployment to staging and production.
  • Collaborate with product managers, data scientists, and engineers to translate business requirements into measurable ML objectives and production deliverables, balancing accuracy, latency, and cost.
  • Implement model serving strategies (online, batch, streaming) and optimize inference performance, latency, and throughput for real-time and near-real-time use cases.
  • Design and operate model monitoring, alerting, and drift-detection systems to track model health, feature distributions, prediction quality, and data integrity in production.
  • Apply software engineering best practices — clean code, modular design, version control (Git), code reviews, and automated testing — to all ML and data components.
  • Integrate models with data warehouses and lakes (e.g., Snowflake, Redshift, BigQuery, S3) and develop efficient SQL and Spark-based transformations for large-scale feature generation.
  • Implement robust data quality pipelines, schema evolution handling, and lineage tracking to ensure traceability from raw data to decisions made by models.
  • Lead or participate in experiments and A/B testing programs to validate model impact and quantify performance improvements against key business metrics.
  • Optimize model lifecycle costs by profiling and rightsizing infrastructure (GPU/CPU, memory), leveraging spot instances, and using efficient batching, quantization, or model distillation techniques.
  • Mentor junior engineers and data scientists on productionizing models, MLOps, and engineering principles, promoting a culture of reliability and reproducibility.
  • Work with security, privacy, and legal teams to ensure ML systems comply with data protection standards, secure data handling, and model access controls.
  • Troubleshoot complex production incidents related to model regressions, pipeline failures, and data anomalies; drive postmortems and remediation plans.
  • Prototype and evaluate new algorithms and tooling (e.g., transformer architectures, graph ML, causal inference) to improve prediction quality or introduce new product capabilities.
  • Design and maintain observability dashboards (Grafana, Looker, Tableau) that present model performance, business KPIs, feature importance, and data quality metrics to stakeholders.
  • Automate model retraining and data refresh schedules while ensuring safe validation and promotion workflows to reduce manual deployment risk.
  • Collaborate with infrastructure and platform teams to containerize models (Docker), orchestrate workloads (Kubernetes), and manage secrets, networking, and scaling policies.
  • Advocate for and lead the implementation of reproducible research-to-production patterns, including experiment metadata recording, dataset versioning, and model lineage tracking.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

  • Python (production-grade code: typing, packaging, testing) and common ML libraries such as scikit-learn, pandas, NumPy.
  • Deep learning frameworks: TensorFlow, Keras, and/or PyTorch for prototyping and production models.
  • SQL expertise for analytics and data pipeline work; ability to author complex, performant queries on large analytic stores.
  • Big data processing frameworks: Spark (PySpark), Dask, or Flink for scalable feature computation.
  • MLOps tools and practices: MLflow, Kubeflow, TFX, or similar for experiment tracking, model registry, and pipelines.
  • Orchestration platforms: Apache Airflow, Prefect, or Argo Workflows for scheduling and dependency management.
  • Containerization and orchestration: Docker, Kubernetes (EKS/GKE/AKS) and knowledge of Helm charts.
  • Cloud infrastructure: hands-on experience with AWS, GCP, or Azure (EC2/GKE/Vertex AI/SageMaker) for model training and serving.
  • Data storage & warehousing: experience with Snowflake, BigQuery, Redshift, Delta Lake, and S3/GCS.
  • Model deployment and serving frameworks: TensorFlow Serving, TorchServe, Seldon, BentoML or custom microservices.
  • CI/CD and IaC: GitHub Actions, Jenkins, Terraform, Pulumi for automated deployments and reproducible infrastructure.
  • Observability and monitoring: Prometheus, Grafana, ELK/EFK, Datadog, and model monitoring frameworks for drift and anomaly detection.
  • Feature stores and data versioning tools: Feast, Pachyderm, or DVC for reproducible feature and dataset management.
  • Performance optimization: profiling inference latency, memory, GPU utilization; techniques like batching, caching, and model quantization.
  • API design and production microservices: building secure, scalable APIs for consumption by downstream systems.
  • Statistical modeling and experimentation: A/B testing, hypothesis testing, causal inference basics to measure model impact.
  • Experience with NLP, computer vision, recommendation systems, or time-series forecasting depending on product domain.

Soft Skills

  • Strong cross-functional communication: explain technical tradeoffs to product managers and business stakeholders.
  • Product-minded: translate model predictions into measurable business value and commercial KPIs.
  • Problem-solving and analytical thinking: break down ambiguous problems and design pragmatic engineering solutions.
  • Ownership and accountability: lead end-to-end delivery of complex ML features and services.
  • Collaboration and mentorship: coach junior team members and work effectively in distributed teams.
  • Prioritization and time management: balance technical debt, feature development, and operational reliability.
  • Adaptability and continuous learning: stay current with fast-evolving ML and MLOps ecosystems.
  • Attention to detail and data-driven decision making.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, Data Science, or related quantitative field.

Preferred Education:

  • Master’s degree or PhD in Machine Learning, Computer Science, Statistics, or a related discipline for research-heavy or advanced modeling roles.

Relevant Fields of Study:

  • Computer Science
  • Statistics / Applied Mathematics
  • Machine Learning / Artificial Intelligence
  • Data Science / Informatics
  • Engineering (Software, Electrical)

Experience Requirements

Typical Experience Range:

  • 3–7+ years of professional experience combining software engineering, data engineering, and machine learning.

Preferred:

  • 5+ years with production ML deployment experience, demonstrable end-to-end project ownership, and proven impact on product metrics. Experience in cloud-native architectures, distributed data processing, and MLOps toolchains is highly desirable.
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