Opkey | Series B Funded | Noida, India (In-Office) | Full-Time
The Opportunity
Opkey, a Series B funded enterprise application lifecycle management platform, is looking
for a Senior Data Scientist to join our team in Noida. We need someone who can build
predictive models, design machine learning algorithms, and extract insights that transform
how enterprises manage Oracle Fusion, Workday, and SAP.
We're not pitching a visionwe're scaling a reality. Our platform already processes
hundreds of gigabytes of enterprise data. Now we need a scientist who can make that data
predict the future. This is your chance to be part of building something that will define a
category.
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 system
integrators. They trust us with hundreds of gigabytes of their most sensitive enterprise
datapayroll files, configuration exports, test resultsbecause we've proven we can turn
that data into intelligence they can't get anywhere else.
We're already doing what others are only talking about. Our systems already compare
millions of payroll records. Our platform already validates enterprise configurations at scale.
Our AI already helps organizations manage application lifecycles that used to take armies of
consultants.
Now we're scaling. And we need exceptional people to help us go from category creator to
category leader.
This is founder mode, not corporate mode. We move fast, we solve hard problems, and
we ship things that matter.
Why This Role Matters
Enterprise data is everywherebut insight is rare. Organizations have terabytes of payroll
runs, configuration snapshots, and test results, but no way to know what it means or what's
coming 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 actionable
insights. When you build a model that predicts payroll errors before they happen, real people
get paid correctly. When your algorithm identifies configuration risks, you prevent outages
that would affect thousands.
This is already happening at Opkey. You'll help us do it smarter, faster, and at a scale
no 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 the
machine 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 datasetsmillions of payroll records, thousands of
configuration parametersand 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 lifecyclenot
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 non
technical users understand the data.
Skills & Qualifications
Required Technical Skills
- Python for Data Science: 4+ years of production experience with PythonPandas,
NumPy, Scikit-learn, and either TensorFlow or PyTorch
- Machine Learning Expertise: Hands-on experience building and deploying ML
modelsregression, classification, clustering, anomaly detection, time-series
forecasting
- Statistical Analysis: Deep foundation in statisticshypothesis 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 impactyour models will affect real enterprise operations
- The opportunity to be part of historybuilding the intelligence layer that defines
how enterprises manage their most critical applications
We've built the data infrastructure. Now we need someone to make it
intelligent.
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 inclusive
environment for all employees.
Skills: pytorch,numpy,tensorflow,hadoop,spark,python,pandas
