Data Analyst — Autonomous Driving

QCraft AI’s interview process typically consists of an online aptitude test followed by a technical interview focusing on SQL queries, data manipulation in Python, and statistical concepts. The final HR round assesses cultural fit and communication skills. Candidates should practice real‑world data scenarios and be ready to discuss autonomous‑driving datasets.

QCraft AI promotes a collaborative, fast‑paced environment where engineers and analysts work side‑by‑side on real‑time autonomous driving challenges. Employees enjoy a healthy work‑life balance with flexible hybrid schedules and ample opportunities for continuous learning and career growth.

Bachelor's degree in Computer Science, Statistics, Mathematics, Economics, or related field; Minimum 60% aggregate (or CGPA 6.0/10); No backlogs at the time of joining; Open to graduates of 2022‑2025 batches; Freshers and candidates with up to 3 years of relevant experience are welcome.

Round 1: Aptitude & Logical Reasoning → Round 2: Technical Interview (SQL, Python, Statistics) → Round 3: HR Interview

QCraft AI is a fast‑growing global leader in advanced driver‑assistance systems (ADAS) and autonomous driving solutions. Founded in Silicon Valley in 2019, the company quickly expanded its footprint to China and now aims to dominate the worldwide market with its L2++ to L4 autonomous driving stack. QCraft’s technology combines high‑definition mapping, sensor fusion, and AI‑driven perception to enable vehicles to navigate complex urban environments safely. The firm’s culture emphasizes innovation, rapid prototyping, and cross‑functional collaboration, making it an exciting place for engineers and analysts who want to shape the future of mobility. At QCraft AI’s Bengaluru office, the Data Analyst – Autonomous Driving role is designed for fresh graduates and early‑career professionals who are passionate about turning massive streams of sensor data into actionable insights. You will work closely with software engineers, perception scientists, and product managers to ensure that the self‑driving system learns from every mile driven. The position offers exposure to cutting‑edge autonomous vehicle technology, hands‑on experience with large‑scale data pipelines, and the chance to influence product decisions that directly affect vehicle safety and performance. Key Responsibilities: 1. Analyze multi‑terabyte autonomous driving datasets to uncover patterns in vehicle behavior, sensor health, and system reliability. 2. Develop and maintain interactive dashboards and periodic reports that track key performance indicators such as disengagement rates, lane‑keeping accuracy, and perception latency. 3. Collaborate with engineering teams to define data collection requirements, schema designs, and validation rules. 4. Perform statistical tests and hypothesis‑driven analysis to evaluate the impact of algorithm updates and hardware changes. 5. Build automated ETL pipelines using Python and SQL to streamline data ingestion, cleaning, and transformation. 6. Document data sources, analysis methodologies, and findings in clear, reproducible formats. 7. Present insights to cross‑functional stakeholders through visual storytelling and concise executive summaries. 8. Contribute to data‑quality initiatives, including anomaly detection and data‑governance processes. 9. Support the development of predictive models that anticipate sensor failures or performance degradations. 10. Stay updated with the latest trends in autonomous driving, AI, and data analytics to continuously improve analytical approaches. Tech Stack: SQL, Python (pandas, NumPy, scikit‑learn), Tableau/Power BI, Git, Linux, cloud storage (AWS S3), Jupyter notebooks. Growth Path: Successful analysts can progress to Senior Data Analyst, Data Science Engineer, or Product Analytics Lead, with opportunities to transition into autonomous‑driving research or engineering roles. Why Join QCraft: You will be part of a visionary company at the forefront of self‑driving technology, work with world‑class talent, and see the real‑world impact of your analyses on vehicles that are reshaping transportation. The hybrid work model offers flexibility while fostering strong team collaboration.