LEAD DATA SCIENTIST

at  Ralta Technologies RT

QUALIFICATIONS:

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Minimum of 5 years of hands-on experience in data analytics, machine learning, or similar roles. Experience in Medical Device or Pharmaceutical industry a plus.
• Minimum of 2 years experience in team leadership or management roles required.
• Proven expertise in designing, implementing, and optimizing ETL pipelines for data integration and transformation across diverse data sources.
• Strong programming skills in Python, including experience with libraries such as pandas, NumPy, scikit-learn, and TensorFlow for data analysis and machine learning.
• Demonstrated ability to write and optimize complex SQL queries, manage relational databases, and ensure data quality and integrity.
• Hands-on experience creating interactive dashboards and visualizations in Power BI to translate data insights into actionable business decisions.
• A solid track record in designing and deploying predictive and prescriptive models to solve real-world business challenges.
• Experience leading and mentoring data teams, fostering a collaborative environment, and guiding junior data scientists in technical skill development.
• Strong project management capabilities, with excellent communication skills to collaborate with cross-functional teams and effectively present findings to stakeholders.

• Design and Oversee ETL Pipelines: Lead the design, implementation, and optimization of Extract, Transform, Load (ETL) processes to ensure efficient and accurate data flow from various sources to analytical platforms.
• Data Analysis and Modeling: Conduct advanced data analysis and develop predictive and prescriptive models using Python, leveraging libraries like pandas, NumPy, scikit-learn, and TensorFlow.
• Database Management and Querying: Design and manage relational databases, write and optimize SQL queries for data extraction and transformation, and ensure database integrity and performance.
• Data Visualization and Reporting: Create interactive dashboards and insightful visualizations using Power BI to communicate findings and support data-driven decision-making across the organization.
• Project Management and Collaboration: Collaborate with cross-functional teams, including data engineers, business analysts, and stakeholders, to align data analytics projects with organizational goals and ensure timely delivery.
• Mentoring and Skill Development: Provide technical guidance and mentorship to junior data scientists, fostering skill development in Python, SQL, ETL processes, and analytics best practices.
• Continuous Improvement: Identify opportunities to enhance analytics workflows, tools, and strategies to improve data accessibility, analysis, and overall business impact.