Data Scientist I

We are looking for an experienced Data Scientist I with comprehensive knowledge of cloud platforms, model optimization, and the full data science lifecycle. In this role, you will be responsible for developing, deploying, and maintaining machine learning models on cloud infrastructure. You will also play a key role in data preprocessing, feature engineering, and performance optimization to ensure models deliver maximum value to our clients.

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Key Responsibilities:

• End-to-End Model Development:
  • Take ownership of the entire data science pipeline, from data preprocessing and feature engineering to model training, tuning, and deployment.
  • Work closely with stakeholders to translate business requirements into technical solutions and data models.
• Data Cleaning and Preprocessing:
  • Perform data cleansing, transformation, and enrichment to prepare datasets for modeling.
  • Identify and address data quality issues, missing values, and outliers to ensure robust model performance.
  • Conduct exploratory data analysis (EDA) to discover patterns, correlations, and insights.
• Model Deployment and Cloud Optimization:
  • Deploy models on cloud platforms (e.g., AWS, Azure, GCP) with a focus on scalability, efficiency, and reliability.
  • Utilize cloud-native tools and best practices to optimize model performance and cost-effectiveness.
  • Monitor and maintain models post-deployment, implementing continuous learning and retraining where necessary.
• Performance Optimization and Maintenance:
  • Optimize model hyperparameters and feature selection to achieve optimal accuracy, speed, and resource utilization.
  • Set up automated monitoring for model drift, performance metrics, and data consistency.
  • Update and improve models periodically based on new data and business needs.
• Collaboration and Knowledge Sharing:
  • Work closely with data engineers, developers, and business analysts to ensure seamless integration of models into production systems.
  • Share insights, best practices, and technical knowledge with team members to foster a culture of continuous improvement.
  • Participate in code reviews and contribute to the development of reusable libraries and templates.
• Documentation and Reporting:
  • Maintain thorough documentation for each step of the data science pipeline, from data preparation to model deployment.
  • Generate reports and dashboards to communicate model performance and business impact to stakeholders.

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Qualifications:

• Education:
  • Master’s degree in Data Science, Computer Science, Statistics, Engineering, or a related field.
• Experience:
  • 3+ years of experience as a Data Scientist or Machine Learning Engineer with end-to-end model development and deployment experience.
  • Strong experience with data preprocessing, feature engineering, and model optimization.
  • Hands-on experience deploying models in cloud environments (AWS, Azure, or GCP).
• Skills and Abilities:
  • Programming: Proficiency in Python and SQL; familiarity with libraries like Pandas, NumPy, Scikit-Learn, TensorFlow, or PyTorch.
  • Cloud Proficiency: Experience with cloud services (AWS Sagemaker, Azure ML, Google Cloud AI) for model training, deployment, and monitoring.
  • Data Wrangling and Preprocessing: Strong skills in data cleaning, transformation, and feature engineering.
  • Model Deployment: Knowledge of containerization tools (Docker, Kubernetes) and CI/CD pipelines for model deployment.
  • Optimization: Proficient in hyperparameter tuning, feature selection, and model performance improvement techniques.
  • Analytical Mindset: Strong problem-solving and analytical skills with the ability to interpret complex data and make data-driven decisions.
  • Communication: Excellent written and verbal communication skills, with the ability to present technical concepts to non-technical stakeholders.
• Preferred Qualifications:
  • Familiarity with MLOps practices for model lifecycle management.
  • Experience with version control (Git) and collaboration tools.
  • Knowledge of data visualization tools (e.g., Power BI, Tableau) for reporting insights and model results.

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What We Offer:

• Innovative Environment: Join a forward-thinking team working with the latest in data science and AI.
• Growth Opportunities: Access to continuous learning and career advancement in the rapidly growing field of AI and ML.
• Flexible Work Options: Remote work opportunities and flexible hours.
• Competitive Compensation: Attractive salary package with performance-based bonuses and benefits.