Data Scientist / Analyst Consultant

Solutionec Core Principles

• *Solutionec** is a global healthcare technology and consulting firm that partners with industry leaders to deliver data\-driven transformation and meaningful patient outcomes. We empower informed decisions through advanced analytics and personalized solutions—believing data is the spark for innovation and growth.

• *Our Core Principles:*** **Innovation** – New ideas and technologies

• **Velocity** – Fast, high\-impact delivery

• **Quality** – Excellence in every outcome

• **Empathy** – Real\-world impact at the center

• *Together, we’re reshaping the future of healthcare.**

Design, develop and deploy statistical and machine learning models for fraud, waste and abuse detection on scheme claims and card data.

Perform advanced data analytics including clustering, classification, profiling, anomaly detection, predictive modelling, scoring, regression, link prediction, co\-occurrence grouping, similarity matching and causal modelling.

Develop fraud detection triggers using advanced analytics techniques such as NLP, OCR, Social Network Analysis and deep learning, and document trigger construct, logic and thresholds for review and approval.

Apply both supervised and unsupervised learning approaches, training models on labelled historical fraud data and scheme ecosystem data.

Conduct data profiling, quality assessment, cleaning, standardisation and feature engineering to create analysis\-ready datasets.

Execute triggers and models on claim and card data within defined turnaround timelines and generate case\-level suspicious output with trigger reasons and evidence fields.

Validate model outputs, assess false positive risk, calibrate thresholds and measure trigger efficacy against defined performance benchmarks.

Fine\-tune and retrain models on the basis of subsequent events, reviewer feedback and confirmed fraud outcomes.

Collaborate with medical experts, business analysts, subject matter experts and technology teams to translate domain knowledge and clinical rules into executable analytical logic.

Prepare model documentation, trigger submission packs, dashboards and reports for internal review committees and client\-facing evaluations.

• *Experience**

Minimum 5 years of experience in analytics and managing big data, out of which at least 3 years in developing AI/ML models.

Demonstrable credentials of having worked on and developed AI/ML models independently.

Experience in fraud analytics, anomaly detection or risk modelling will be preferred.

Experience in healthcare, health insurance or government health scheme data will be an added advantage.

• *Required Competencies**

Strong proficiency in Python and/or R for building statistical and machine learning models.

Proficiency in SQL and experience working with large\-scale / big data environments.

Hands\-on experience with machine learning techniques such as neural networks, decision trees, regression, anomaly detection and clustering.

Experience with natural language processing, text mining, entity extraction and processing of unstructured data sources including documents and images.

Understanding of cloud\-based analytics environments and native AI/ML services (AWS preferred).

Strong grounding in statistical methods, sampling, hypothesis testing and model validation techniques.

Ability to work with structured and unstructured healthcare or transactional datasets and establish linkage across multiple source systems.

Experience translating business and domain rules into executable analytical logic and production\-ready code.

Proficiency in data visualisation and reporting tools for presenting analytical outcomes.

Strong analytical, problem\-solving and documentation skills.

Excellent communication and stakeholder management abilities, including the ability to explain model logic to non\-technical audiences.

• *Preferred Skills**

Experience in healthcare analytics, health insurance claims analytics or fraud, waste and abuse detection.

Experience in fraud analytics roles with a proven track record of developing and implementing fraud detection models and strategies.

Knowledge of AB\-PMJAY / health benefit packages / claims adjudication workflows.

Exposure to Social Network Analysis, graph analytics or link analysis for collusion and network detection.

Familiarity with OCR, image analytics and document forgery or duplication detection.

Experience with model deployment, versioning and MLOps practices.

Exposure to dashboarding and BI tools such as Power BI, Tableau or equivalent.