DataBricks - ML Flow - Data Scientist - Machine Learning

Job Description – Data Scientist / Senior Data Scientist / Principal Data Scientist

Location: Bangalore (On-Site)

Experience Range: 4 to 15+ Years

Joining: Immediate / Within 1 Week Preferred

Work Timings: 10:00 AM – 6:00 PM

About the Role

We are hiring across multiple levels — Data Scientist, Senior Data Scientist, and Principal Data Scientist — for our Bangalore office.

We are looking for professionals with a strong background in Machine Learning, Artificial Intelligence, Data Science, and hands-on experience building and deploying scalable ML solutions.

Candidates with IoT or sensor-based ML experience will be given preference (not mandatory).

If you are an immediate joiner and passionate about solving complex real-world problems using data, we would love to connect.

Key Responsibilities

For All Levels (4–15 years):

Develop, train, optimize, and deploy Machine Learning and AI models for production environments.

Work on end-to-end ML pipelines, including data collection, preprocessing, feature engineering, model development, evaluation, and deployment.

Collaborate with cross-functional teams including engineering, product, and business to deliver impactful solutions.

Build scalable solutions using Python, ML frameworks, and cloud-based tools.

Conduct data analysis, visualization, and experimentation for business insights.

Senior & Principal Levels:

Architect and lead the design of highly scalable ML systems.

Mentor junior data scientists and drive best practices in MLOps, experimentation, and research.

Lead complex AI/ML initiatives and provide technical direction.

Work with leadership to define ML/AI roadmap and strategy.

Required Skills

Mandatory:

Strong expertise in Machine Learning, AI, Deep Learning, and Data Science.

Hands-on experience with Python, ML libraries (TensorFlow, PyTorch, Scikit-learn, etc.).

Experience with data pipelines, model training, model deployment, and evaluation at scale.

Solid understanding of statistics, algorithms, and predictive modelling.

Strong experience with SQL, data engineering basics, and cloud platforms (AWS/Azure/GCP).

Preferred (Not Mandatory):

IoT / sensor data analytics, predictive maintenance, time-series modelling.

Exposure to MLOps tools (Docker, Kubernetes, MLflow, Airflow).

Experience with Generative AI / LLMs is a plus.

Who Should Apply?

Professionals with 4 to 15+ years of experience in ML/AI/Data Science.

Candidates available to join immediately or within 1 week.

Individuals who are open to onsite work in our Bangalore office.

People looking for career growth from Data Scientist → Senior → Principal roles.

Compensation

We offer a competitive salary based on market standards, along with a strong hike for the right candidate.

How to Apply

Interested candidates who can join immediately or within next week can do the below test and email with resume at:

TEST

*Test*

Please solve the below and share the ipynb file from Jupiter note book with Markdowns and examples or share Google Collab link. Please run the code and test it before sending..

Question 1: Data Preprocessing Pipeline (OOP + Modular Design)
• Abstract base classes and inheritance
• Multiple preprocessing classes (missing values, outliers, normalization)
• Pipeline pattern for chaining operations
• Proper fit/transform paradigm

Question 2: Advanced Pandas Operations
• Complex groupby with custom aggregations
• DateTime manipulation and feature extraction
• Rolling statistics and window functions
• Multi-dimensional pivot tables
• Growth rate calculations

Question 3: NumPy Vectorization & Broadcasting
• Cosine similarity without loops
• Euclidean distance using broadcasting
• Pearson correlation with matrix operations
• Efficient large-scale computations

Question 4: Feature Engineering (Pandas + NumPy)
• Polynomial features using NumPy power functions
• Interaction terms (multiplication, division)
• Binning and discretization
• Lag features for time series
• Rolling statistical features

Question 5: Data Validation System
• Missing value detection
• Duplicate checking
• Schema validation
• Outlier detection (IQR and Z-score methods)

Question 6: Generative AI – LLM Fundamentals & Prompt Engineering

• Transformer architecture and tokenization
• Zero-shot, one-shot, and few-shot prompting
• Chain-of-Thought and context window management
• Prompt templates with role and variable injection
• Factuality, coherence, and bias evaluation

Question 7: Fine-Tuned & RAG-Enhanced LLM Systems

• Domain adaptation using LoRA/QLoRA fine-tuning
• Embedding generation and vector database creation
• Retriever–Ranker–Generator pipeline for RAG
• Context engineering and hallucination mitigation
• Evaluation using and Faithfulness metrics

Question 8: Agentic AI & Multi-Agent Collaboration

• Agent roles: planner, executor, validator, critic
• Multi-agent orchestration via LangChain or CrewAI
• Memory persistence: episodic, semantic, procedural
• Tool-use reasoning and self-reflection loops
• Success metrics: autonomy, cooperation, task completion

*Please attempt all questions even if you don't know some*

*Please keep all examples in context of Kingfisher Beer manufacturing factory OR any large B2B commercial bank. Select one theme*

Please note knowledge of ML Flow and DataBricks would be highly appreciated

*Please share your resume , expected CTC and in how many days you can join after getting an offer letter*

*All positions are in the office in Bangalore*