DA
Data Core Systems Technical Recruiter
Talent Acquisition Partner
🏢 Data Core Systems • Direct Recruiter Email:
recruiter@datacoresystems.com
Required Skills & Tech Stack
AI-Generated Response Evaluation
Subject Matter Expertise
Stem
LaTeX
Critical Thinking
Literature Review
Mathematical Programming
Research Workflow
📚 Free Study Materials (4)
📖
Aptitude Practice Questions & Mock Tests
Open Resource ↗
Curated logical, quantitative, and verbal reasoning problems for the initial online screening round.
🎯
Company-Specific Interview Preparation Corner
Open Resource ↗
Detailed interview experiences, exam formats, and previous test questions for Data Core Systems and tech roles.
📝
Technical Placement Cheat Sheets & Question Bank
Open Resource ↗
High-yield coding cheat sheets, core CS fundamentals (OOP, DBMS, OS, Networks), and rapid revision guides.
💻
Algorithm, DSA & Live Code Debugger Practice
Open Resource ↗
Hands-on problem sets to improve coding speed and step-by-step memory debugging.
Eligibility Criteria
Experience: 0-2 Yrs. Open to matching qualifications.
Selection & Interview Process
1
Online Assessment / Technical Interview Rounds / HR Review
Job Description & Key Responsibilities
We are looking for highly qualified PhD holders with python, faculty members and experienced Subject Matter Experts for specialized, project-based assignments involving scientific research, STEM problem-solving, scientific reasoning, AI response evaluation, technical content development and subject-matter validation . Key Responsibilities Conduct advanced research and scientific analysis. Create and validate complex STEM problems and solutions. Develop research workflows, evaluation criteria and technical content. Evaluate AI-generated responses for scientific accuracy and reasoning. Perform literature reviews and evidence-based analysis. Provide specialized subject-matter validation and technical expertise. Required Qualifications Completed PhD in a relevant STEM discipline, such as Physics, Chemistry, Biotechnology, Mathematics, Engineering, or a related field. Working knowledge of at least one programming language, preferably Python, R, MATLAB, or C++. Demonstrable experience applying programming in doctoral research or professional projects. Experience in computational analysis, simulations, data processing, numerical methods, or algorithm development. Ability to independently develop or modify code rather than relying exclusively on prebuilt software. Technical Skills Essential Skills Programming: Python, R, MATLAB, C++, C, Java, or Julia. Research coding: Custom scripts, computational research projects, and algorithm implementation. Data analysis: Data processing, statistical analysis, and research dataset analysis. Scientific computing: Computational modelling, numerical methods, or simulations. Additional / Preferred Skills Data analysis libraries: Pandas, NumPy, and SciPy. Machine learning and AI: Scikit-learn, PyTorch, TensorFlow, deep learning, or NLP, depending on project requirements. Software development: Git/GitHub, debugging, testing, algorithms, and data structures. Databases: SQL, PostgreSQL, or MySQL. Automation: Research workflow automation and data pipelines. High-performance computing: Linux, HPC, parallel computing, CUDA, or cloud computing, where relevant. Candidate Experience Levels Level 1 Basic Coding: Uses programming for research calculations, plotting, basic statistical analysis, and simple scripts. Level 2 Intermediate / Research Coding: Develops research scripts, implements algorithms, performs advanced data analysis, builds simulations, and automates workflows. Level 3 – Advanced Coding: Develops complex software, optimizes algorithms, writes tested code, works with large datasets, and applies software engineering practices. CV Screening Criteria Candidates will be evaluated based on: Completion of a PhD in a relevant STEM discipline. Proficiency in at least one programming language. Practical coding experience in research or professional projects. Experience with computational analysis, simulations, data processing, or algorithm development. Evidence of independently developing or modifying code, supported by specific projects, research work, or professional achievements. Important: Merely listing Python, MATLAB, or another programming language in the CV is not sufficient. Candidates should demonstrate how they used programming to solve research problems or achieve measurable outcomes. Suitable Candidate Profiles STEM PhD researchers with hands-on programming experience. Computational scientists and scientific programmers. Researchers experienced in numerical modelling and simulations. STEM researchers with strong data analysis and automation skills. Advanced coding candidates with software development or AI/ML experience, where relevant to the project. Mandatory Resume Details Full Name | Highest Qualification | Specialization/Research Area | Current Designation | Relevant Experience | Research Experience | Publication Details | Google Scholar URL | H-index | LinkedIn URL | Contact Number | Email ID
Preparation Hub
Data Core Systems — AI & Machine Learning Interview Guide
Previously asked questions, exam syllabus, coding benchmarks & round strategy.
📖 Previously Asked Questions & Preparation Links
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
🎯
Data Core Systems Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Data Core Systems 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 ↗
💻 Previously Asked Coding Challenges
Easy / Medium
⚡ Live Debugger
Two Sum & Two Pointers Array Optimization
Asked in 85% tech rounds
Medium
⚡ Live Debugger
Reverse Linked List & Detect Cycle (Floyd's Algorithm)
Frequently asked in core rounds
Medium
⚡ Live Debugger
SQL Nth Highest Salary with DENSE_RANK() & Subqueries
Top standard database question
Easy / Medium
⚡ Live Debugger
Valid Parentheses & Stack Implementation
Standard online assessment problem
🗺️ Round-Wise Strategy
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 (AI-Generated Response Evaluation, Subject Matter Expertise, Stem, LaTeX, Critical Thinking, Literature Review, Mathematical Programming, Research Workflow) & 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
Data Core Systems Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
❓ Core Technical Questions & Answers
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).
🤝 Behavioral & HR Questions (STAR Method)
Why do you want to join Data Core Systems as a SME - Phd in STEM?
Preparation Tip: Highlight Data Core Systems's market reputation, recent tech innovations, and how your skills in AI-Generated Response Evaluation 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.
