Data Analyst - AI & Robotics

As a Data Analyst, you will work closely with AI/ML teams, robotics engineers, and data leads to process, analyze, and visualize large datasets. Your primary goal is to extract meaningful insights that improve robotic system performance, enable predictive maintenance, and drive automation intelligence. This role requires expertise in data analysis, visualization, statistical techniques, and cross-functional collaboration to support AI and industrial automation initiatives.

Key Responsibilities

1. Data Analysis & Insights

• Analyze large-scale structured and unstructured datasets from IoT devices, sensors, ERP systems, and robotic platforms.
• Identify patterns, trends, and anomalies using statistical techniques and machine learning to improve robotic operations.
• Conduct predictive and prescriptive analytics to support decision-making and process optimization.
• Develop data models that enhance system intelligence, efficiency, and automation capabilities.

2. Data Preparation & Feature Engineering

• Clean, preprocess, and transform raw data to maintain high data integrity and consistency.
• Handle missing values, outliers, and inconsistencies to ensure data readiness for AI models and analytics.
• Work with AI/ML teams to engineer relevant features from sensor and industrial data, enhancing model accuracy.
• Support the development of real-time data pipelines for continuous analytics and monitoring.

3. Data Visualization & Reporting

• Develop interactive dashboards and reports using Tableau, Power BI, or custom-built solutions.
• Use advanced data visualization techniques to represent complex datasets clearly and effectively.
• Present insights to cross-functional teams (AI, Robotics, Software) to align data-driven strategies with business objectives.

4. AI/ML & Predictive Maintenance Support

• Collaborate with AI/ML engineers to provide data insights for developing and training machine learning models.
• Support predictive maintenance by analyzing historical and real-time data to reduce system downtime and failures.
• Monitor deployed AI models, ensuring continuous improvement and accuracy.

5. Cross-Functional Collaboration & Innovation

• Work closely with AI/ML teams, Robotics engineers, and Software developers to integrate data insights into automation systems.
• Promote a data-driven culture, ensuring analytics-driven decision-making across teams.
• Stay updated with emerging data analytics, visualization, and machine learning tools, implementing best practices.

6. Data Governance & Compliance

• Implement data governance policies to ensure data security, accuracy, and compliance with industry standards.
• Maintain detailed documentation of analytical processes, methodologies, and key findings.

Required Qualifications - Education & Experience

• Bachelors degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
• 2+ years of experience in data analysis, preferably in robotics, industrial automation, or manufacturing industries.

Technical Skills

• Data Analysis & Visualization:
• Expertise in Python, R, SQL, and data visualization tools like Tableau, Power BI.
• Strong knowledge of Pandas, NumPy, Matplotlib, and Seaborn for analytics.
• Statistical & Machine Learning Techniques:
• Experience with hypothesis testing, regression analysis, and clustering methods.
• Familiarity with feature engineering and basic ML model development.
• Big Data & Cloud Technologies (Preferred):
• Experience working with relational databases (MySQL, PostgreSQL).
• Knowledge of big data frameworks (Hadoop, Spark) and cloud platforms (AWS, Azure, GCP) is a plus.
• Industrial & IoT Data Processing:
• Experience handling data from robotics systems, IoT devices, and industrial automation platforms.

Preferred Qualifications

• Domain Knowledge: Experience with robotics data, IoT analytics, or industrial automation processes.
• Project Management: Familiarity with Agile workflows, JIRA, and task management tools.
• AI/ML Support: Basic knowledge of machine learning workflows and predictive analytics.