KASHII UPDATEZ Everyday Student Requirements & Python Coding Tutorials by Python Kashi
REQUIREMENT_ID_92 • 3-DAY_ACTIVE_POLICY

Data Scientist/Analyst

Company
Company Turing
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary 12 LPA – 18 LPA (depending on experience and skill set)
Location
Location Remote
Posted Date
Posted Date Yesterday
Python Data Analysis Data Science Machine Learning Jupyter Notebooks Pandas NumPy Statistical Modeling Problem Solving Communication
📖
Aptitude Practice Questions Open Resource ↗
Helps sharpen quantitative and logical reasoning skills essential for data‑driven problem solving at Turing.
📖
AI & Data Science Concepts Open Resource ↗
Covers core topics like model evaluation, fine‑tuning, and RLHF that are directly relevant to the role.
📖
Data Scientist Interview Prep Open Resource ↗
Provides role‑specific interview questions and answer frameworks to ace Turing’s technical round.
📖
Algorithm Practice Problems Open Resource ↗
Offers a wide range of coding challenges to improve Python proficiency and problem‑solving speed.

Bachelor’s or Master’s degree in Engineering, Computer Science, Mathematics, Statistics or related field; minimum 60% aggregate (or CGPA 6.0/10); graduating batch 2024‑2026; no active backlogs at the time of joining; strong command of English (both spoken and written).

1
Round 1: Technical interview (60 mins)
2
Round 2: Cultural fit & offer discussion (15 mins)
Turing is one of the world’s fastest‑growing artificial‑intelligence companies, dedicated to accelerating the development and deployment of powerful AI systems. By partnering with leading AI labs, Turing pushes the boundaries of model capabilities in reasoning, coding, multimodality, and more. The company also translates these breakthroughs into real‑world solutions for Fortune‑500 customers, tackling mission‑critical challenges across industries. With a culture that blends cutting‑edge research with product‑focused engineering, Turing offers a unique environment where innovators can see their ideas move from prototype to production at scale. The Data Scientist/Analyst role is crafted for fresh graduates or early‑career professionals who have a strong foundation in Python and a passion for turning raw data into actionable insights. You will work remotely with US‑based clients, helping them build and fine‑tune large language models (LLMs) and other AI products. Your day‑to‑day will involve writing clean, reproducible Python code, exploring public datasets (Kaggle, UN, US government portals), and documenting findings in Jupyter notebooks or similar platforms. Strong analytical thinking, business sense, and clear communication are essential, as you will regularly interact with researchers, annotators, and product teams. Key responsibilities include: 1. Design, develop, and maintain high‑quality Python code for training and optimizing AI models. 2. Conduct systematic evaluations (Evals) to benchmark model performance and suggest improvements. 3. Rank AI model responses against predefined criteria and generate detailed rationales. 4. Lead supervised fine‑tuning (SFT) initiatives by creating and curating task‑specific datasets. 5. Collaborate with researchers and annotators to implement Reinforcement Learning with Human Feedback (RLHF). 6. Invent innovative evaluation strategies that align models with user needs and ethical standards. 7. Produce clear, concise documentation and peer‑review code for continuous quality enhancement. 8. Respond to business queries using public datasets, delivering insights that drive decision‑making. 9. Stay updated with emerging tools, techniques, and best practices in AI training pipelines. 10. Participate in cross‑functional meetings to influence product road‑maps and model improvements. Tech stack: Python, Jupyter, Pandas, NumPy, Scikit‑learn, PyTorch/TensorFlow, Git, REST APIs, cloud platforms (AWS/GCP). The role offers a clear growth path—from junior analyst to senior data scientist, and eventually to AI research engineer or product lead—depending on performance and learning appetite. Joining Turing means exposure to world‑class AI research, mentorship from seasoned scientists, and the chance to contribute to products used by industry giants like Nvidia, Disney, and Reddit. The fully remote setup, flexible hours, and collaboration with global teams make it an attractive launchpad for anyone eager to shape the future of artificial intelligence.

Turing — AI & Machine Learning Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC 📖
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource ↗
COMPANY GUIDE 🎯
Turing Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Turing and off-campus tech roles.

Open Resource ↗
MOCK TESTS & PAPERS 📝
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource ↗
CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource ↗
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Python, Data Analysis, Data Science, Machine Learning, Jupyter Notebooks, Pandas, NumPy, Statistical Modeling, Problem Solving, Communication) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Turing Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the bias-variance tradeoff and how do you prevent overfitting? Answer ▼
Model Answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer ▼
Model Answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer ▼
Model Answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer ▼
Model Answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Why do you want to join Turing as a Data Scientist/Analyst?
Preparation Tip: Highlight Turing's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

More Fresh Requirements in Software & Tech

View Category Feed ↗
Turing
Data Scientist/Analyst
Apply Apply Now ↗
Chat Chat with Kashii