Hypersoft Technologies Limited

Company Description Hypersoft Technologies Limited is a publicly traded company specializing in Software Products and Professional IT Services. We provide financial and business applications with extensive user installations, along with consultancy services and custom software development. Our core competencies include Client/Server Software Development, Database Applications, and Rapid Application Development for Web and Windows platforms. We offer scalable, efficient, and reliable solutions across various market segments including securities management, ERP systems, and BPO operations. Our commitment is to complete projects on time, maintaining high standards of quality and fostering strong client relationships. Role Description Proactively engage with client/business stakeholders (e.g., Sales, Marketing, Market Access, Commercial Operations, Medical Affairs, Patient Advocacy teams) to deeply understand their challenges and strategic objectives. Explore, clean, and prepare large, complex, and sometimes messy datasets from various sources, including but not limited to: sales data, prescription data, claims data, Electronic Health Records (EHRs), patient support program data, CRM data, and real-world evidence (RWE) datasets. Design, develop, validate, and deploy robust statistical models and machine learning algorithms (e.g., predictive models, classification, clustering, time series analysis, causal inference, natural language processing).• Create impactful data visualizations, dashboards, and presentations using tools like Tableau, Power BI, or Python/R/Alteryx visualization libraries. Support the entire data science lifecycle, from conceptualization and data acquisition to model development, deployment (MLOps), and ongoing monitoring in production environments Strong programming skills in Python (e.g., Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch) and/or R for data manipulation, statistical analysis, and machine learning. Expertise in SQL for data extraction, manipulation, and analysis from relational databases. Experience with machine learning frameworks and libraries. Proficiency in data visualization tools (e.g., Tableau, Power BI) and/or visualization libraries (e.g., Matplotlib, Seaborn, Plotly). Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop) is a significant advantage. Specific experience with Natural Language Processing (NLP) techniques, Generative AI models (e.g., Transformers, diffusion models) Language Models (LLMs), and prompt engineering is highly desirable. Experience with fine-tuning LLMs, working with models from Hugging Face, or utilizing major LLM APIs (e.g., OpenAI, Anthropic, Google). Experience with MLOps practices and tools (e.g., MLflow, Kubeflow, Docker, Kubernetes). Knowledge of pharmaceutical or biotech industry regulations and compliance requirements like HIPAA, CCPA, SOC, etc.