Career
Join our team and grow with us — from fresh graduates to seasoned professionals.
Join CoolMinds!
CoolMinds Technologies is a fast growing organization that values and encourages hard working workforce in the IT industry. We are a combination of unquestionable wisdom and humility. CoolMinds Technologies is an equal opportunity employer. If you want to challenge your capabilities and conquer the unexplored territories in the IT industry, then CoolMinds Technologies is the right place for you!
Our experienced team identifies the right talent, nurtures it, and helps them grow into technically sound professionals competent to face the challenges of the industry.
Fresh graduates with ME/M Tech/BE/B Tech/MCA degrees are also welcome. Periodic assessments and interviews for different experience level candidates are conducted.
Open Positions
Showing 4 available opportunities across global locations
Senior Analyst - Decision Science
As a Senior Analyst in Decision Science, you will independently drive analyses and support strategy and model initiatives across fraud detection and prevention. You will partner with cross-functional stakeholders to frame problems, evaluate trade-offs between fraud losses and customer friction, and deliver measurable improvements through data-driven decisioning.
Roles & Responsibilities
- Lead end-to-end analytical workstreams: problem definition, data sourcing, methodology selection, validation, and storytelling.
- Design and evaluate fraud strategies (rules/segmentation/thresholds) to optimize loss, approval rates, and operational efficiency.
- Develop and maintain monitoring for strategy and model performance; propose recalibration actions based on drift, new attack vectors, or business changes.
- Partner with data scientists to support feature engineering, model experimentation, and interpretation of model outputs for decisioning.
- Collaborate with product, engineering, operations, and compliance to translate analytics into implementable requirements and controlled rollouts.
- Mentor junior analysts on analytical rigor, documentation, and best practices; contribute to reusable code and templates.
Qualifications
- 2–4 years of experience in decision science, analytics, fraud/risk strategy, or a related quantitative role.
- B.Tech compulsory; MBA/MS preferred.
- Strong SQL skills; proficiency in Python/R; experience with notebooks and version control is a plus.
- Working knowledge of statistics, experimentation, and predictive modeling concepts; ability to explain performance and trade-offs.
- Ability to communicate clearly with technical and non-technical stakeholders and influence decisions through data.
Data Engineer - Data Science
As a Data Engineer in the Decision Science Team, you will build and maintain reliable datasets and pipelines that power fraud analytics, reporting, and AI/ML model development. You will partner with decision science, data science, and engineering teams to ensure data is accurate, timely, governed, and scalable for both real-time and batch use cases.
Roles & Responsibilities
- Design, develop, and optimize data pipelines (batch and/or streaming) to support fraud decisioning, monitoring, and model feature generation.
- Build curated, well-documented datasets and data marts; implement data quality checks, reconciliation, and alerting.
- Work with data scientists to productionize feature pipelines and ensure training/serving consistency and reproducibility.
- Improve performance and cost efficiency through query optimization, partitioning, and scalable storage/compute patterns.
- Support governance requirements including access controls, lineage, and documentation; partner with compliance/security as needed.
- Collaborate with upstream and downstream teams to troubleshoot data issues and deliver stable SLAs for critical fraud datasets.
Qualifications
- 2–4 years of experience in data engineering, analytics engineering, or backend data roles supporting analytics/ML.
- B.Tech compulsory; MBA/MS preferred.
- Strong SQL skills; proficiency in Python/Scala/Java (any one); familiarity with orchestration and CI/CD is a plus.
- Experience with data modeling, ETL/ELT patterns, and data quality/observability practices.
- Ability to partner with analytics and product stakeholders to understand requirements and deliver reliable data products.
Analyst - Decision Science
As an Analyst in Decision Science, you will support fraud analytics initiatives by extracting data, building foundational analyses, and helping develop decision rules and model-driven insights that improve loss performance and customer experience. You will work closely with data scientists, fraud strategists, and engineers to translate business questions into measurable analyses and clear recommendations.
Roles & Responsibilities
- Perform data extraction, cleaning, and validation from large transactional datasets; document assumptions and data quality checks.
- Build exploratory analyses and dashboards to monitor fraud trends, alerts, and loss metrics; identify anomalies and drivers.
- Support development of fraud strategies (rules, thresholds, segments) and help evaluate impact through A/B tests or back-testing.
- Assist in model development lifecycle by preparing features, running experiments, and compiling model performance summaries.
- Create clear, executive-ready summaries of findings and recommendations with guidance from senior team members.
- Collaborate with technology/engineering partners to ensure analytics outputs are reproducible and aligned to production needs.
Qualifications
- 0–2 years of experience in analytics, decision science, risk, fraud, or a related domain (internships/co-ops considered).
- B.Tech compulsory; MBA/MS preferred.
- Working knowledge of SQL and at least one programming language (Python/R) for analysis.
- Understanding of basic statistics and experimentation concepts; ability to interpret model/strategy performance metrics.
- Strong problem-solving skills, attention to detail, and communication skills (written and verbal).
Assistant Manager - Decision Science
As an Assistant Manager in Decision Science, you will lead high-impact analytics workstreams across fraud strategy and model-driven decisioning. You will manage priorities across multiple initiatives, guide junior team members, and partner with product, engineering, operations, legal, and compliance to deliver measurable improvements in fraud losses, approval rates, and customer outcomes.
Roles & Responsibilities
- Own delivery of decision science workstreams (strategy analytics, experimentation, monitoring) from problem framing through implementation and post-launch tracking.
- Lead development and optimization of fraud strategies and decision policies; quantify trade-offs between risk, growth, and customer friction.
- Partner with data science teams to translate model outputs into actionable strategies, including cut-offs, champion/challenger tests, and performance governance.
- Establish and maintain KPI frameworks and ongoing monitoring for loss, approval, operational impact, and model/strategy drift.
- Communicate insights and recommendations to senior stakeholders; drive alignment on roadmap, risk appetite, and execution plans.
- Mentor analysts/senior analysts; review work for analytical rigor, documentation quality, and stakeholder readiness.
Qualifications
- 6–8 years of experience in decision science, fraud/risk analytics, strategy, or applied data science in financial services or a similar data-rich industry.
- B.Tech compulsory; MBA/MS preferred.
- Advanced SQL and strong Python/R skills; ability to work with large datasets and develop robust analytical solutions.
- Demonstrated experience leading projects and influencing cross-functional stakeholders; people mentorship experience is required.
- Strong understanding of model/strategy governance, monitoring, and experimentation best practices.
High-Performance Architectures That Stay Resilient Under Real-World Pressure.
We build stable, high-performance systems that stay up when it matters most.