Full-Time Data Scientist with ML Ops

As a Data Scientist with strong MLOps Engineer, you will play a pivotal role in transforming data into actionable insights through advanced machine learning techniques. You will design and develop complex machine learning models to address key business challenges and drive strategic initiatives. Your deep expertise in MLOps will be critical in ensuring these models are efficiently deployed, monitored, and maintained in real-time production environments. You will build and manage end-to-end data pipelines, from data ingestion and preprocessing to model training and deployment. Additionally, you will collaborate with cross-functional teams to integrate models into our existing systems and continuously optimize their performance. Your role will also involve troubleshooting issues and documenting processes to ensure effective application of best practices in data science and operationalization.

Key Responsibilities:

1. Model Development & Validation:

Design, build, and validate machine learning models tailored to business needs.

Experiment with various algorithms and techniques to improve model accuracy and performance.

Collaborate with data analysts and stakeholders to understand data requirements and objectives.

2. MLOps Implementation:

Develop and implement MLOps strategies to manage the full lifecycle of machine learning models.

Automate the deployment, monitoring, and scaling of ML models using MLOps tools and practices.

Ensure models are deployed in real-time production environments, maintaining high availability and performance.

3. Data Pipeline Development:

Build and manage robust data pipelines to support model training, testing, and deployment.

Design workflows to handle data ingestion, preprocessing, and transformation.

Implement data quality and validation checks to ensure the accuracy and consistency of data used for modeling.

4. Performance Monitoring & Optimization:

Monitor the performance of deployed models in real-time and address any issues related to model drift, degradation, or failures.

Continuously evaluate and optimize model performance through tuning and retraining as needed.

Develop and maintain performance metrics and dashboards to track model effectiveness.

5. Collaboration & Communication:

Work closely with cross-functional teams, including data engineers, software developers, and business stakeholders, to deliver data-driven solutions.

Translate complex technical concepts into actionable insights for non-technical stakeholders.

Provide technical guidance and support to team members as required.

6. Documentation & Knowledge Sharing:

Create and maintain comprehensive documentation for models, pipelines, and MLOps processes.

Share knowledge and best practices with team members to foster a culture of continuous learning and improvement.

Stay updated on industry trends, emerging technologies, and best practices in data science and MLOps.

7. Troubleshooting & Support:

Diagnose and resolve issues related to model performance, deployment, and integration.

Provide ongoing support and maintenance for deployed models and data pipelines.

Conduct root cause analysis and implement corrective actions to address issues.

Must-Have Qualifications:

Educational Background: Bachelor's or master's degree in computer science, Data Science, Engineering, Mathematics, or a related field.

Experience: 6-9 years of experience in data science, with a strong focus on MLOps and productionizing machine learning models.

Programming Skills: Proficiency in Python for data analysis and machine learning.

Machine Learning Expertise: Deep understanding of machine learning algorithms, statistical modeling, and model evaluation techniques.

MLOps Knowledge: Very good knowledge of MLOps principles, tools, and practices, including real-time usage and deployment strategies. Hands-on experience with MLOps platforms such as MLflow, Kubeflow, TensorFlow Serving, or similar.

Cloud Platforms: Experience with major cloud providers (AWS, Azure, Google Cloud) for deploying and managing machine learning models.

Data Engineering Skills: Solid understanding of data engineering principles, including ETL processes, data warehousing, and SQL.

Version Control: Proficiency in using version control systems such as Git for code management.

Communication Skills: Strong verbal and written communication skills with the ability to present technical information to diverse audiences.

Good-to-Have Qualifications:

Big Data Technologies: Experience with big data tools and technologies like Hadoop, Spark, or Kafka.

Containerization & Orchestration: Familiarity with Docker, Kubernetes, or other containerization and orchestration technologies.

DevOps Practices: Knowledge of DevOps methodologies and tools such as Jenkins, Terraform, or CI/CD pipelines.

Business Acumen: Ability to understand and translate business requirements into technical solutions and model designs.