Data Scientist – AI

Jobtailor; Chennai District; On-site; INR 1,500,000 - 2,600,000; Full time

Job summary

Jobtailor is seeking a skilled Data Scientist/ML Engineer to lead end-to-end model development, from EDA to deployment, across cloud environments. You will build reproducible code, validate models, and collaborate with stakeholders to translate business needs into data-driven solutions.

You will also own MLOps practices, construct scalable pipelines, monitor model health, and mentor junior team members while documenting architecture and experiments for long-term maintainability.

Qualifications

• Bachelor's or Master's degree in a quantitative field such as Data Science, CS, or Statistics.
• Proficiency in Python or R and SQL with ML libraries.
• Experience with DS libraries (Scikit-learn) and deep learning frameworks (TensorFlow/PyTorch).

Responsibilities

• Design, execute, and deploy DS projects including EDA and feature engineering.
• Develop robust, reproducible code with proper validation and Git workflows.
• Deploy models via CI/CD; monitor and troubleshoot prediction services.
• Build cloud data pipelines and manage ML services for cost-efficiency.
• Design experiments and dashboards to monitor model health and drift.
• Mentor juniors and communicate findings to stakeholders.
• Create technical design docs and roadmaps for data sources and experiments.

Skills

Communication

Mentoring

Collaboration

Visualization Skills

Business Acumen

Education

Bachelor’s or Master’s degree in Data Science, CS, Statistics or related field

Tools

Python/R

SQL

Scikit-learn

TensorFlow

PyTorch

XGBoost

Git Workflows

Unit Testing

Model Validation

Job description

• End-to-End Model Development: Design, execute, and deploy impactful DS projects. This includes performing Exploratory Data Analysis (EDA), feature engineering, and implementing advanced modeling techniques tailored to unique data characteristics (e.g., XGBoost, TensorFlow, or PyTorch).
• Engineering & Reproducibility: Consistently deliver high-quality, reproducible code. You will implement robust error handling, conduct comprehensive model validation, and establish patterns/standards for code clarity and maintainability within a Git-based workflow.
• MLOps & Deployment: Deploy models using established CI/CD pipelines and tools (e.g., MLflow, Kubeflow). You will be responsible for designing scalable deployment strategies, configuring model monitoring, alerting, and troubleshooting prediction service issues.
• Cloud Operations: Implement data processing pipelines in the cloud (e.g., Spark, Dataflow) and manage ML services effectively. You will optimize cloud resources for cost-efficiency, security, and reliability.
• Experimentation & Observability: Design and execute experiments (A/B tests) and create dashboards to monitor model health, feature drift, prediction distributions, and performance SLOs (Service Level Objectives).
• Collaboration & Influence: Translate business needs into technical tasks and build consensus on analytical approaches. You will mentor junior team members through constructive code/methodology reviews and communicate findings effectively to varied audiences.
• Documentation: Produce clear technical design documents and architectural roadmaps for data sources, transformations, assumptions, and experiments to ensure long-term scalability and maintainability.

Requirements

• Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related quantitative field.
• Technical Mastery: Proficiency in Python/R and SQL. Advanced experience with DS libraries such as Scikit-learn, and exposure to deep learning frameworks (TensorFlow/PyTorch).
• Cloud Proficiency: Hands‑on experience with cloud data storage and compute (e.g., GCS, Vertex AI), build pipelines, and partnering with engineering to operationalize production‑ready models.
• Software Engineering: Strong understanding of Git workflows, unit testing, and advocating for quality tools/practices in Data Science workflows.
• Business Acumen: Proven ability to partner with stakeholders to define data‑driven strategies and quantitatively contribute to business goals via models and insights.
• Communication: Demonstrating strong visualization skills and the ability to socialize model results into strategic implications for leadership, covering both technical and non‑technical audiences.

Hard Skills

• Exploratory Data Analysis
• Feature Engineering
• XGBoost
• TensorFlow
• PyTorch
• SQL
• Scikit-learn
• Git Workflows
• Unit Testing
• Model Validation

Soft Skills

• Communication
• Mentoring
• Collaboration
• Visualization Skills
• Business Acumen