Data Science & Analytics Engineer

Job Overview

We are seeking a highly skilled Data Science & Analytics Engineer with 3 to 11 years of professional experience to drive data-driven decision-making within our organization. In this pivotal role, you will leverage your expertise in data science, statistical analysis, predictive modeling, and data visualization to transform complex datasets into strategic business insights. Collaborating closely with cross-functional teams, including business stakeholders, data engineers, and technology partners, you will design and deploy scalable analytics solutions that enhance operational efficiency and support executive-level decision-making. Your contributions will be instrumental in shaping the future of our data analytics capabilities and driving innovation through advanced analytical methodologies.

Key Responsibilities

As a Data Science & Analytics Engineer, your core responsibilities will include:

• Model Development & Deployment: Design, develop, and deploy predictive and analytical models to address complex business challenges, ensuring optimal performance and scalability.
• Data Exploration & Analysis: Conduct in-depth data exploration, statistical analysis, feature engineering, and model evaluation to uncover actionable insights and trends.
• Visualization & Reporting: Create interactive dashboards, reports, and visualizations using industry-leading tools to effectively communicate analytical findings to both technical and non-technical stakeholders.
• Scalable Analytics Solutions: Develop and implement scalable data analytics solutions utilizing modern cloud and big data platforms to support enterprise-wide initiatives.
• Cross-Functional Collaboration: Partner with business teams to translate strategic objectives into analytical solutions, ensuring alignment with organizational goals and data governance standards.
• Data Quality & Governance: Establish and maintain robust data quality frameworks, ensuring adherence to best practices in data governance, security, and compliance across all analytics initiatives.
• Stakeholder Communication: Present analytical findings, insights, and recommendations in a clear and compelling manner to facilitate informed decision-making at all levels of the organization.
• Process Optimization: Continuously evaluate and enhance analytics processes, reporting capabilities, and model performance to drive operational excellence and innovation.

Required Technical Skills

To excel in this role, you must possess the following technical competencies:

Programming & Data Science

• Strong proficiency in Python or R for data analysis, statistical modeling, and machine learning applications.
• Hands-on experience with data manipulation, exploratory data analysis (EDA), and predictive analytics techniques.

Data Science Platforms

• Extensive experience with Databricks or Dataiku for collaborative data science, machine learning, and end-to-end analytical workflow development.
• Proven ability to design, train, and deploy machine learning models in production environments.

Business Intelligence & Visualization

• Strong expertise in Power BI, Tableau, or Looker for creating dynamic dashboards and executive-level reports.
• Capability to transform raw data into visually compelling narratives that drive strategic decision-making.

Data Warehousing & Analytics

• Experience with BigQuery or Snowflake for large-scale data warehousing and analytics.
• Proficiency in SQL for data extraction, transformation, and query optimization to support analytical initiatives.

Analytics & Machine Learning

• Comprehensive knowledge of statistical analysis, forecasting, classification, clustering, and regression modeling techniques.
• Familiarity with machine learning libraries, model evaluation metrics, and performance tuning methodologies.

Qualifications

To qualify for this role, candidates must meet the following criteria:

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Information Technology, or a related quantitative field.
• Minimum of 3 to 11 years of professional experience in Data Science, Business Analytics, or Advanced Analytics.
• Demonstrated expertise in statistical analysis, predictive modeling, and data visualization.
• Exceptional analytical, mathematical, and problem-solving skills with a keen attention to detail.
• Outstanding communication and presentation abilities to convey complex insights to diverse audiences.
• Proven experience working in Agile/Scrum environments, fostering collaboration and iterative development.

Preferred Skills & Certifications

While not mandatory, the following skills and certifications will enhance your candidacy:

• Experience with cloud analytics platforms on AWS, Microsoft Azure, or Google Cloud Platform.
• Knowledge of MLOps, model deployment, and AI-driven analytics to streamline machine learning workflows.
• Familiarity with ETL/ELT processes and data engineering concepts to support data pipeline development.
• Exposure to Generative AI, Large Language Models (LLMs), or AI-assisted analytics for advanced problem-solving.
• Relevant certifications in Data Science, Analytics, or Cloud technologies (e.g., AWS Certified Data Analytics, Google Professional Data Engineer, or Microsoft Certified: Azure Data Scientist Associate).

Key Technology Stack

The ideal candidate will have hands-on experience with the following technologies:

• Programming Languages: Python or R
• Data Science Platforms: Databricks or Dataiku
• Business Intelligence: Power BI, Tableau, or Looker
• Data Warehouse: BigQuery or Snowflake
• Analytics: Statistical Analysis, Predictive Modeling, Machine Learning, Data Visualization
• Cloud Platforms: AWS, Microsoft Azure, or Google Cloud Platform (Preferred)