Data Scientist
Location: Hyderabad, India – 5 days a week onsite requirement
Experience: 3–5 Years
Company: Circuitry.ai
About Circuitry.ai
Circuitry.ai is an Enterprise AI SaaS company building advanced AI systems for the US SLM sector.
We specialize in:
- Production-grade RAG architectures
- Fine-tuned LLM systems
- Knowledge Graph–driven AI
- Agentic AI frameworks
- Trust-centric AI systems with explainability and evidence
Our focus is solving the Trust in AI problem for enterprises by combining LLMs, Graph Retrieval, structured data systems, and advanced evaluation pipelines.
We are building AI systems that go beyond demos — real deployments, real customers, real scale.
Role Overview
We are looking for a hands-on Data Scientist with strong experience in classical machine learning, statistical modeling, and real-world production systems. This role is focused on model selection, algorithm design, feature engineering, and evaluation, rather than data engineering, visualization, or MLOps.
You will work closely with business and product teams to translate real-world problems into ML solutions, select the right modeling approach, and drive end-to-end model development—from hypothesis to production-ready models.
Key Responsibilities
- Problem Framing & Business Understanding
- Translate business problems into well-defined ML problems
- Formulate hypotheses and validate them using statistical techniques
- Clearly communicate model assumptions, trade-offs, and outcomes to stakeholders
Model Development & Selection
- Design and build supervised and unsupervised ML models
- Select appropriate algorithms based on data characteristics and problem type
- Strong focus on:
- Regression models (linear, regularized)
- Tree-based models (Random Forest, Gradient Boosting)
- Ensemble techniques (Bagging, Boosting, XGBoost)
- Understand when and why to use each model
Feature Engineering (Critical Requirement)
- Design and implement robust feature engineering pipelines
- Work with structured and semi-structured datasets
- Apply domain-driven feature transformations
Model Evaluation & Experimentation
- Define proper evaluation metrics aligned with business goals
- Perform rigorous model validation (cross-validation, backtesting)
- Conduct A/B testing and statistical significance testing
Scalability & Performance
- Experience working with large datasets and distributed training frameworks
- Optimize models for performance and generalization
Production Readiness (Not MLOps-heavy)
- Deliver models that are ready for production integration
- Collaborate with engineering teams for deployment (no need to own MLOps)
Must-Have Skills
- Strong expertise in:
- Ensemble methods (Bagging, Boosting, XGBoost)
- Regression techniques
- Feature engineering (critical)
- Model evaluation and validation strategies
- Solid understanding of:
- Bias-variance tradeoff
- Overfitting/underfitting
- Model interpretability
- Hands-on experience with:
- Python (NumPy, Pandas, Scikit-learn, XGBoost, LightGBM)
- SQL for data extraction
- Experience with distributed training or large-scale data processing
Good to Have
- Time Series Analysis (ARIMA, Prophet, forecasting models)
- Exposure to GenAI/LLMs (not mandatory)
- Experience in domain-specific ML applications (manufacturing, operations, etc.)
Experience Required
- 3+ years of hands-on experience in:
- Building ML models in real-world systems
- Working across development → evaluation → production usage
- Proven track record of solving business problems using ML models
Education
- Bachelor's or Master's in Computer Science, Statistics, Mathematics, or related field
What Success Looks Like
- Ability to choose the right model for the right problem
- Strong intuition on data → features → model → evaluation
- Can independently drive end-to-end ML problem solving
- Bridges business understanding with statistical rigor
Qualifications
- Bachelor's degree or equivalent experience in quantative field (Statistics, Mathematics, Computer Science, Engineering, etc.)
- At least 1 - 2 years of experience in quantitative analytics or data modeling
- Deep understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms
- Fluency in a programming language (Python, C,C++, Java, SQL)
- Familiarity with Big Data frameworks and visualization tools (Cassandra, Hadoop, Spark, Tableau)
Please share your portfolio at [Confidential Information]
Thanks
Satya Prasad
HRBP Manager
[HIDDEN TEXT]
8019088854
