Senior Data Scientist

Codvo.ai; Pune District; Hybrid; INR 1,500,000 - 2,500,000; Full time

Job summary

Codvo.ai is hiring a Data Scientist with hands-on Predictive Intelligence Engine (PIE) experience to turn enterprise data into predictive models that forecast outcomes and prevent failures. You will own problems end to end—from framing questions to production deployment with engineering teams—and work directly with client stakeholders to drive business impact.

The role requires 4–9 years of hands-on data science, strong time-series forecasting, and proficiency in Python, SQL, and cloud

Qualifications

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering or a related field.
• 4–7 years of hands-on data science experience, with at least 3 years building predictive models.
• Hands-on experience building or working on a Predictive Intelligence Engine (PIE) in production.
• Strong time-series forecasting skills (ARIMA, Prophet, LSTM or gradient-boosted models such as XGBoost / LightGBM).
• Strong Python skills, including pandas, NumPy, scikit-learn and TensorFlow or PyTorch.
• Advanced SQL and experience working with large datasets.
• Solid grounding in statistics, probability, model validation and performance metrics.
• Working knowledge of a cloud platform: Azure, AWS or GCP.
• Clear communication skills and comfort working with client stakeholders.

Responsibilities

• Design, build and enhance models and pipelines within the Predictive Intelligence Engine (PIE), partnering with stakeholders to translate problems into measurable predictive use cases.
• Build, validate and tune predictive models for forecasting, failure prediction, risk scoring, churn and demand.
• Develop predictive maintenance and anomaly detection solutions using sensor, IoT and time-series data from industrial assets.
• Engineer features from large structured and unstructured datasets, including historian and operational data.
• Apply statistical methods, survival analysis and experiment design to measure model impact.
• Build and evaluate GenAI / LLM solutions such as RAG pipelines where they strengthen predictive workflows.
• Work with data and ML engineers to deploy models into production and monitor accuracy, drift and retraining (MLOps).
• Present predictions and their business value to technical and non-technical audiences through dashboards and clear recommendations.
• Document models, assumptions and results, following data governance and compliance standards.
• Mentor junior team members and contribute to reusable predictive assets and best practices.

Skills

Time-series forecasting

Python

SQL

Statistics

Communication

Education

Bachelor's/Master's in CS/Stats/Math/Data Science

Tools

TensorFlow

PyTorch

scikit-learn

Pandas

NumPy

MLflow

Docker

Job description

Location: Pune, India (hybrid)

Experience : 4-9 years

Type: Full-time

About Codvo.ai

Codvo.ai is an AI-native technology services company that helps global enterprises build and scale data, cloud and AI solutions.

Role overview

We are hiring a Data Scientist with hands-on Predictive Intelligence Engine (PIE) experience to turn enterprise and industrial data into models that forecast outcomes and prevent failures. You will own problems end to end: framing the question, building and validating models, and putting them into production with engineering teams. The role works directly with client stakeholders and suits someone who enjoys both technical depth and business impact.

Key responsibilities

• Design, build and enhance models and pipelines within the Predictive Intelligence Engine (PIE), partnering with stakeholders to translate problems into measurable predictive use cases.
• Build, validate and tune predictive models for forecasting, failure prediction, risk scoring, churn and demand.
• Develop predictive maintenance and anomaly detection solutions using sensor, IoT and time-series data from industrial assets.
• Engineer features from large structured and unstructured datasets, including historian and operational data.
• Apply statistical methods, survival analysis and experiment design to measure model impact.
• Build and evaluate GenAI / LLM solutions such as RAG pipelines where they strengthen predictive workflows.
• Work with data and ML engineers to deploy models into production and monitor accuracy, drift and retraining (MLOps).
• Present predictions and their business value to technical and non-technical audiences through dashboards and clear recommendations.
• Document models, assumptions and results, following data governance and compliance standards.
• Mentor junior team members and contribute to reusable predictive assets and best practices.

Required qualifications

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering or a related field.
• 4–7 years of hands-on data science experience, with at least 3 years building predictive models.
• Hands-on experience building or working on a Predictive Intelligence Engine (PIE) in production, such as predictive maintenance, demand forecasting or failure / risk prediction.
• Strong time-series forecasting skills (ARIMA, Prophet, LSTM or gradient-boosted models such as XGBoost / LightGBM).
• Strong Python skills, including pandas, NumPy, scikit-learn and TensorFlow or PyTorch.
• Advanced SQL and experience working with large datasets.
• Solid grounding in statistics, probability, model validation and performance metrics.
• Working knowledge of a cloud platform: Azure, AWS or GCP.
• Clear communication skills and comfort working with client stakeholders.

Preferred qualifications :

• Experience with industrial data sources such as AVEVA PI / OSIsoft PI historians, SCADA, IoT and sensor streams.
• Experience with real-time or streaming prediction (Kafka, Spark Streaming, Azure Event Hubs).
• MLOps tools such as MLflow, Docker, Kubernetes, Azure ML, SageMaker or Databricks.
• Experience with GenAI and LLMs: RAG, vector databases, LangChain / LlamaIndex or agentic AI workflows.
• Visualization tools such as Power BI or Tableau.
• Domain exposure to Oil & Gas, Energy, LNG, Manufacturing or Healthcare / MedTech.
• Experience in a client-facing consulting or services environment.