AI Tech Lead

Key Responsibilities:

1. Data Preparation & Engineering:

• Extract, clean, transform and process messy, data from various sources to prepare it for analysis and modeling.
• Help to reduce manual effort, improve efficiency, enhance data quality, and ensure the reliable and timely delivery of data for reports and dashboards through automation.
• Supports: The foundational work of a Data Engineer or Analytics Engineer, ensuring data readiness and quality.

2. Business Acumen & Problem Solving:

• Translate complex business questions into well-defined data science problem and analytical tasks.
• Perform rigorous Exploratory Data Analysis (EDA) to understand data characteristics, identify patterns, trends, uncover hidden insights, potential data
• quality issues, and formulate hypotheses.
• Supports: The strategic thinking and analytical rigor of a Data Scientist or highly skilled Data Analyst, bridging the gap between business needs and technical solutions.

3. Data Analysis & Insight Generation (Technical):

• Should be able to synthesize large datasets into actionable insights using SQL, Python, DAX, PowerQuery, and statistical tools.
• Apply appropriate statistical methods, data mining techniques, and machine learning algorithms (where applicable) to address specific business needs (e.g., prediction, classification, clustering, forecasting).
• Use Large language models (LLMs) to get insights and generate summaries from data by analyzing text and identifying patterns in data.
• Supports: The core analytical work of a Data Scientist, leveraging a wide array of technical tools and advanced methodologies to extract meaningful intelligence.

4. Business Intelligence & Visualization:

• Develop and maintain robust data models, interactive dashboards, and reports using BI tool to support real-time decision-making.
• Ensure visualizations effectively communicate key performance indicators (KPIs), trends, and insights, enabling users to explore data and make data-driven decisions independently.
• Supports: The responsibilities of a Business Intelligence Developer or Analyst, focusing on making data accessible and understandable for business users.

5. Project Management, Leadership & Collaboration:

• Manage and lead multiple data projects independently with best practices, ensuring timely delivery of high-impact analytics and ad hoc reporting."
• Continuously iterate on data models and approaches based on performance feedback and evolving business requirements.
• Stay up to date with the latest advancements in business intelligence, data science, machine learning, AI, and relevant technologies.
• Work with data scientists and analytics engineers in the team to integrate AI solutions into existing systems and workflows.
• Collaborate cross-functionally with the business and internal team.
• Supports: The leadership, strategic oversight, continuous improvement, and cross-functional communication aspects typically found in a Senior or Lead role within data analytics or data science.

Required Skills and Experience:

• Min 5+ years of experience in Business Intelligence or Advanced Data Analytics
• Bachelor’s or Master’s degree in Data Science, Statistics, Business Analytics, or a related field.
• Advanced proficiency in SQL, preferably in Google Big Query to query, clean, manipulate, arrange, and present data
• Hands-on experience with BI tools, preferably MS Power BI
• Strong experience in DAX and Power Query
• Experience with Python for Advanced Analytics and Large language models (LLMs)
• Familiarity with cloud computing platforms, preferably Google Cloud