Senior Data Engineer

• Conducting extensive data exploration, analysis, and preprocessing to ensure data quality.
• Developing and applying data science methodologies to extract insights from large-scale structured and unstructured datasets.
• Utilizing predictive analytics, time series forecasting, and statistical models to drive business decision-making.
• Performing feature engineering and selection to optimize model performance.
• Training, evaluating, and optimizing models using machine learning and statistical techniques.
• Deploying models to production environments, ensuring robustness and scalability.
• Monitoring model performance and defining strategies for identifying drift; retraining or refining models as needed.
• Collaborating with cross-functional teams to integrate data science models with business applications and systems.
• Staying updated on the latest advancements in data science technologies.
• Leading and mentoring junior team members.
• Developing and maintaining comprehensive documentation for data workflows and analytical processes.

Key Performance Indicators (KPIs) for the role:

Over the next 12 months, this role’s success will be measured on:

• Successful deployment of data science models into production.
• Improvement in model performance metrics (e.g., accuracy, precision, recall).
• Effective data-driven decision-making supported by predictive analytics and statistical models.
• Timely identification and mitigation of model drift.
• Effective collaboration with cross-functional teams.
• Mentorship and development of junior team members.

KEY JOB REQUIREMENTS:

In this role, you will be successful if you have:

Experience:

• 5+ years of experience in data science.
• Strong understanding of data science techniques, including statistical modeling and data analytics.
• Experience with data science libraries (e.g., NumPy, pandas, scikit-learn).

Skills & Competencies:

Must Have:

• Proficiency in Python, R, or other relevant programming languages.
• Proficiency in working with large datasets, data wrangling, and data preprocessing.
• Ability to work independently and lead projects from inception to deployment.
• Experience with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, GCP, Azure)