01
TRPD-26-04877
Data Scientist (AI/ML & MLOps)
Hiring a Data Scientist (5–12 years) to build scalable AI/ML predictive models and advanced analytics solutions, and to operationalize them via end-to-end ML pipelines and MLOps (CI/CD, versioning, monitoring, governance). Strong Python (NumPy/Pandas/Scikit-learn plus TensorFlow or PyTorch), statistics (hypothesis testing, regression/classification/clustering/forecasting), SQL, and experience with large-scale/distributed data are required. Cloud exposure (AWS/Azure/GCP) is expected; Databricks (Mosaic AI/MLflow/Delta Lake), Snowflake Cortex/ML, GenAI/LLMs, and Docker/Kubernetes are advantageous.
Position summary
- Location
- India
- Workplace
- Hybrid
- Employment
- Full Time
- Experience
- Minimum 5 years and Maximum 12 years
Role overview
Why This Role Matters.
Hiring a Data Scientist (5–12 years) to build scalable AI/ML predictive models and advanced analytics solutions, and to operationalize them via end-to-end ML pipelines and MLOps (CI/CD, versioning, monitoring, governance). Strong Python (NumPy/Pandas/Scikit-learn plus TensorFlow or PyTorch), statistics (hypothesis testing, regression/classification/clustering/forecasting), SQL, and experience with large-scale/distributed data are required. Cloud exposure (AWS/Azure/GCP) is expected; Databricks (Mosaic AI/MLflow/Delta Lake), Snowflake Cortex/ML, GenAI/LLMs, and Docker/Kubernetes are advantageous.
Your Impact
Deliver Enterprise Value
Help organisations solve complex business problems through modern technology, consulting expertise and measurable outcomes.
Collaboration
Work Across Teams
Collaborate with consultants, architects, engineers and client stakeholders throughout the project lifecycle.
Growth
Learn Continuously
Gain exposure to enterprise technologies, certifications, mentoring and real-world project experience.
Career Path
Grow With Ubique
Build a long-term consulting career with opportunities to take on greater responsibility and leadership over time.
Responsibilities
What You'll Be Doing.
Every role at Ubique contributes directly to solving meaningful business challenges for our clients.
02
Perform advanced statistical analysis and build predictive models to derive actionable insights.
03
Develop and implement end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, and deployment.
04
Build and manage MLOps frameworks for continuous integration, delivery, monitoring, and model governance.
05
Work closely with data engineering teams to ensure robust and scalable data pipelines.
06
Conduct exploratory data analysis (EDA) and hypothesis testing to support data-driven decision-making.
07
Optimize model performance through hyperparameter tuning and advanced techniques.
08
Deploy and monitor models in production environments ensuring performance, reliability, and scalability.
09
Collaborate with cross-functional teams including business stakeholders, architects, and product owners.
10
Stay updated with the latest advancements in AI/ML, GenAI, and data science tools and frameworks.
Technology stack
Tools & Technologies.
The platforms and technologies you'll use to build modern, enterprise-grade solutions.
01
Python
Required
02
NumPy
Required
03
Pandas
Required
04
Scikit-learn
Required
05
TensorFlow
Required
06
PyTorch
Required
07
SQL
Required
08
MLOps
Required
09
CI/CD
Required
10
Model monitoring
Required
11
Model versioning
Required
12
Feature engineering
Required
13
Distributed computing
Required
14
Databricks
Required
15
Mosaic AI
Required
16
MLflow
Required
17
Delta Lake
Required
18
Snowflake
Required
19
Snowflake Cortex
Required
20
Snowflake ML
Required
Requirements
Skills & Experience.
We value curiosity, collaboration and continuous learning. If you don't meet every requirement but believe you can make an impact, we'd still love to hear from you.
Essential
Required Qualifications
Python (NumPy, Pandas, Scikit-learn)
TensorFlow or PyTorch
Machine Learning algorithms (supervised, unsupervised)
Deep learning (exposure/experience)
Statistical modeling and analysis
Hypothesis testing
Regression, classification, clustering
Forecasting / time-series modeling
Predictive modeling and advanced analytics
End-to-end ML pipelines (ingestion, feature engineering, training, validation, deployment)
MLOps practices (CI/CD, model versioning, monitoring, deployment)
SQL
Large-scale datasets and distributed computing frameworks
Cloud platforms (AWS or Azure or GCP)
Exploratory Data Analysis (EDA)
Preferred
Nice to Have
Databricks (Mosaic AI, MLflow, Delta Lake)
Snowflake Cortex / Snowflake ML capabilities
Generative AI / LLM-based applications
Model explainability
Fairness and governance frameworks
Docker
Kubernetes
Advanced AI/ML or Data Science certifications
Industry exposure: Healthcare, Retail, BFSI, Manufacturing
What you'll gain
More Than Just A Job.
We're committed to helping every team member grow professionally, personally and technically while working on meaningful projects.
Global Exposure
Collaborate with international clients and multicultural teams on enterprise programmes.
Continuous Learning
Expand your expertise through mentoring, certifications and hands-on project experience.
Career Growth
Take ownership, develop leadership skills and grow your consulting career over time.
Flexible Working
Hybrid and remote collaboration designed around trust and delivering exceptional outcomes.
People First
Join a supportive culture where collaboration, respect and long-term relationships come first.
Enterprise Projects
Work on meaningful technology initiatives for leading organisations across industries.
Apply
Apply for Data Scientist (AI/ML & MLOps)
One page, about two minutes. We only ask for what we actually need to have a first conversation.