Opkey | Series B Funded | Noida, India (In-Office) | Full-Time
The Opportunity
Opkey, a Series B funded enterprise application lifecycle management platform, is lookingfor a Senior Data Scientist to join our team in Noida. We need someone who can buildpredictive models, design machine learning algorithms, and extract insights that transformhow enterprises manage Oracle Fusion, Workday, and SAP.We're not pitching a vision—we're scaling a reality. Our platform already processeshundreds of gigabytes of enterprise data. Now we need a scientist who can make that datapredict the future. This is your chance to be part of building something that will define acategory.
About Us
Opkey is redefining how enterprises manage the lifecycle of their most critical applications.We've built the platform that takes organizations from Design to Configure to Test to Train,powered by agentic AI.Our customers already include Fortune 500 companies and top global systemintegrators. They trust us with hundreds of gigabytes of their most sensitive enterprisedata—payroll files, configuration exports, test results—because we've proven we can turnthat data into intelligence they can't get anywhere else.We're already doing what others are only talking about. Our systems already comparemillions of payroll records. Our platform already validates enterprise configurations at scale.Our AI already helps organizations manage application lifecycles that used to take armies ofconsultants.Now we're scaling. And we need exceptional people to help us go from category creator tocategory leader.This is founder mode, not corporate mode. We move fast, we solve hard problems, andwe ship things that matter.
Why This Role Matters
Enterprise data is everywhere—but insight is rare. Organizations have terabytes of payrollruns, configuration snapshots, and test results, but no way to know what it means or what'scoming next.We've built the infrastructure. Now we need the intelligence.You'll be the person who turns raw enterprise data into predictions, patterns, and actionableinsights. When you build a model that predicts payroll errors before they happen, real peopleget paid correctly. When your algorithm identifies configuration risks, you prevent outagesthat would affect thousands.This is already happening at Opkey. You'll help us do it smarter, faster, and at a scaleno one else has achieved.
What You'll Do
You'll join a team that's already processing enterprise data at scale. Your job is to build themachine learning models and statistical algorithms that extract intelligence from that data:
- Build Predictive Models: Develop ML models that predict payroll discrepancies,
configuration failures, and test regressions before they happen. Use techniques like
regression, classification, anomaly detection, and time-series forecasting.
- Design Anomaly Detection Algorithms: Create statistical models that identify
meaningful variances in massive datasets—millions of payroll records, thousands of
configuration parameters—and distinguish signal from noise.
- Develop Machine Learning Pipelines: Build end-to-end ML pipelines from feature
engineering to model training to production deployment. Own the full lifecycle—not
just notebooks, but deployed, monitored, production systems.
- Run Experiments & A/B Tests: Design and execute experiments to validate
hypotheses, measure model performance, and continuously improve prediction
accuracy.
- Extract Cross-Enterprise Insights: Apply clustering, pattern recognition, and
statistical analysis to identify best practices, failure modes, and benchmarks across
hundreds of implementations.
- Communicate Insights to Stakeholders: Translate complex model outputs into
clear, actionable recommendations. Build visualizations and reports that help nontechnical users understand the data.
Skills & Qualifications
Required Technical Skills
- Python for Data Science: 4+ years of production experience with Python—Pandas,
NumPy, Scikit-learn, and either TensorFlow or PyTorch
- Machine Learning Expertise: Hands-on experience building and deploying ML
models—regression, classification, clustering, anomaly detection, time-series
forecasting
- Statistical Analysis: Deep foundation in statistics—hypothesis testing, probability
distributions, A/B testing, regression analysis
- Feature Engineering: Ability to transform raw data into meaningful features that
improve model performance
- SQL Proficiency: Comfortable writing complex queries to extract, transform, and
analyze data from relational databases
- Model Deployment: Experience taking models from notebooks to production—
MLOps concepts, model monitoring, and performance tracking
Nice to Have
- Experience with deep learning and neural networks
- Background in natural language processing (NLP)
- Exposure to enterprise applications (Oracle, Workday, SAP)
- Experience with big data tools (Spark, Hadoop) for large-scale model training
- Knowledge of data visualization tools (Matplotlib, Seaborn, Plotly, Tableau)
Mindset & Approach
- Hypothesis-Driven: You start with questions, not tools. You design experiments to
test ideas and let data guide decisions.
- Production-Oriented: You understand that a model in a notebook has zero
business value. Impact comes from deployed systems.
- Business Translator: You can explain what a model does and why it matters to
non-technical stakeholders.
- Founder Mentality: You thrive in ambiguity, make decisions with incomplete
information, and care about outcomes over process.
- Continuous Learner: ML is evolving fast. You stay current with new techniques and
know when to apply them.
What We're NOT Looking For
- People who only want to build models but not deploy them
- Those who can't explain their work to non-technical audiences
- Anyone who needs perfect data before they can start (enterprise data is messy)
- Candidates who optimize for algorithmic elegance over business impact
What We Offer
- Competitive salary + meaningful equity in a company that's already winning
- The chance to build ML systems that Fortune 500 companies depend on
- A team that values speed, ownership, and results over politics
- Direct impact—your models will affect real enterprise operations
- The opportunity to be part of history—building the intelligence layer that defines
how enterprises manage their most critical applications
We've built the data infrastructure. Now we need someone to make itintelligent.Apply with your resume and a brief note about a predictive model you've built and deployed.Opkey is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusiveenvironment for all employees.Skills: pytorch,numpy,tensorflow,hadoop,spark,python,pandas
