Data Science Consulting Services

Data Science as a Service (DSaaS) lets companies tap into advanced analytics – including deep learning – without needing to build out an in-house data science team. Rather than hiring, training, and maintaining complex internal capabilities, businesses gain access to expert data science skills whenever needed. With DSaaS, you can optimize business processes through dependable forecasting, root cause analyses, and regular or on-demand insights that drive performance improvement.

A data scientist helps companies turn raw information into business value. Their work usually starts with preparing and analyzing data to uncover patterns and trends. From there, they design and train predictive models, validate performance, and integrate solutions into real business processes. Alongside model development, they create dashboards and visualizations that make results easy to understand, and ensure long-term efficiency by monitoring and fine-tuning systems. In many cases, data scientists also explore emerging technologies like generative AI or computer vision to bring innovative ideas into practice.

At Quantum, we see data science as a powerful driver of change across industries, helping our clients move from intuition to evidence-based decision-making. 

• FinTech: data-driven risk assessment and management, personalized financial planning, portfolio optimization solutions, compliance and regulatory reporting automation etc
• Healthcare: Virtual health assistants and chatbots, medical imaging solutions, predictive healthcare, etc.
• Manufacturing / Infrastructure: predictive maintenance, defect detection with computer vision
• Agritech: yield forecasting, crop maturity detection, satellite/drone data analysis
• Smart Cities / Geospatial: anomaly detection, surveillance, traffic & infrastructure analytics

Quantum distinguishes itself by:

• End-to-end delivery: from strategy to data pipelines, modeling, deployment, and ongoing optimization.
• Running a Data Science Center of Excellence, continuous experimentation with emerging AI technologies
• Accelerated delivery: using pre-built modules and reusable components that shorten time-to-market.
• Cross-industry experience: industry-specific use cases in finance, healthcare, agritech, infrastructure, etc.
• Research & Development: our R&D team curates and incubates an innovation pipeline, sourcing ideas from projects, industry trends, and using a proven methodology to validate, mature, and integrate them into client solutions or new products.

The cost of a data science solution depends on the scope of the project, the complexity of the models, and the maturity of your data. Small-scale models or components typically start in the range of $30,000 to $200,000. Mid-scale initiatives usually fall between $200,000 and $400,000. Full-scale end-to-end projects with integration can exceed $400,000 and go well beyond $600,000 depending on the requirements.

Several factors influence the final price: the quality and volume of your data, how much preprocessing is needed, the level of accuracy and robustness expected from the models, and the complexity of integrating the solution with existing enterprise systems.