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Area Sales Manager

Description : About the job Role Title : State Program Manager, Cardiovascular - Manipur Required Work Experience : 3+ years Roles and Responsibilities : - The ideal candidate should be detail-oriented, a multi-tasker with excellent organisational skills. - Ensure all tasks of the project are processed, organised, and also progressed according to the project guidelines. - Coordination with the Hub (JNIMS) related to the clinical data and Standard Operating Procedure. - Coordination with the internal & external stakeholders for smooth operation and better outcome of the project. - Assign the tasks to the relevant teams and ensure timely completion. - Develop and maintain project performance databases that track overall progress and the accomplishment of specific milestones. - Monitor and assign resources appropriately to maximise project efficiency and streamline deliverable outputs. - Forecasting project outcomes and/or risks to the appropriate management channels as needed, escalating issues promptly based on project work plans. - Develop an understanding of the landscape of cardiovascular diseases in the targeted geography with respect to disease burden, existing solutions, healthcare providers, gaps and challenges. - Map healthcare system structures with the project objectives. - Handle the entire state project in line with the objectives. - Engage with key govt/ institutional stakeholders at the state level across government bodies, building relationships with key opinion leaders, stakeholders, and institutional-level officials. - Lead the strategic dialogue with the key health officials, cardiologists, technical committee, and other key stakeholders on existing gaps and how our solution can help manage Cardiovascular issues in the public health facilities in the state. - Schedule weekly/monthly review meetings with internal and external stakeholders Skill Set (Technical and Soft skills) : - Min 3 years of experience in a healthcare institution. - Proven analytical, organisational, and problem-solving abilities. - Strong MS Office Skills (PowerPoint, Word, Excel) and IT Literacy. - Resilient and willing to adapt. - Skilled in prioritising and meeting deadlines without compromising on a high standard of quality. - Excellent verbal and efficient written communication, demonstrating confidence in a client-facing role. - Ability to foster collaborative relationships with customers and stakeholders. Expected Educational Qualification : - MBA/MPH/ MHA - Any Master's with a Healthcare background

Others

Tricog Health - State Program Manager - Healthcare Domain 

About the job : We are looking for a curious and passionate Machine Learning Engineer to join our high-impact team. You'll work directly on ML models that analyze cardiac data, helping doctors save lives. This role offers the unique opportunity to see your work make a tangible difference in patient outcomes while building state-of-the-art ML infrastructure. What You'll Do : - Design and Implement ML Models : Develop, train, and evaluate machine learning and deep learning models for cardiac datasets and associated metadata. - Data Pipeline Development : Work with large, complex, and sometimes messy clinical datasets. Contribute to building robust and scalable data pipelines for data ingestion, cleaning, feature engineering, and labeling. - Model Deployment and MLOps : Deploy models to production environments and monitor their performance in real-world clinical settings. Build and maintain REST APIs for model inference using frameworks like FastAPI or Flask. - Design scalable API endpoints with proper request validation, error handling, and authentication. Implement and maintain robust MLOps practices, including version control, continuous integration/continuous deployment (CI/CD), and model monitoring in a production environment (e.g., cloud platforms like AWS, Azure, or GCP). - Performance Optimization : Optimize model performance for inference speed and resource efficiency, crucial for deployment on various platforms (cloud, edge devices). - Collaboration : Work collaboratively with software engineers, data scientists, and clinical domain experts to translate clinical needs into technical requirements and deliver high-impact solutions. - Documentation and Research : Maintain detailed documentation of models, code, and experiments. Stay current with the latest research in deep learning, medical image analysis, and time-series analysis. - Regulatory and Compliance : Develop documentation in order to comply with regulatory requirements such as CDSCO, FDA etc. Required : - Experience : 1-3 years of professional experience as a Machine Learning Engineer, Data Scientist, or a related role. - Programming : Proficiency in Python and experience with core data science libraries (NumPy, pandas, scikit-learn). - Deep Learning Frameworks : Hands-on experience with at least one major deep learning framework (PyTorch). - MLOps Basics : Familiarity with MLOps principles, including containerization (Docker) and cloud service experience (AWS, Azure, or GCP). - Understanding of model evaluation metrics, cross-validation, and debugging ML systems. - Ability to read and implement research papers. Preferred Skills : - Experience with healthcare data. - Understanding of statistical methods and experimental design for model validation. - Experience with structured training pipelines such as Pytorch Lightning. - Knowledge of regulatory requirements for medical devices (FDA, CE marking). - Experience with cloud platforms (AWS, GCP, Azure) and serverless deployments. - Publications or contributions to ML open-source projects. - -

Bangalore

Tricog Health - Machine Learning Engineer - Data Science 

Description : About the job : ABOUT TRICOG : At Tricog, we are seeking a Product Manager to act as a crucial bridge between our commercial and engineering teams. As our organization grows, the complexity of communication between those who sell our products and those who build them increases. The Product Manager creates an effective abstraction between these groups, ensuring seamless alignment and execution. The successful candidate will deeply understand the objectives, constraints, and preferences of both the business and technical sides of the organization. This role is not just about building features; it's about understanding how to create significant clinical impact while simultaneously delivering measurable financial and business impact. You will be expected to understand how product changes affect the company's Profit & Loss (P&L) statement and work across teams to improve it over time, increasing revenue impact and reducing costs. You will own the entire product life cycle, from identifying a problem to measuring its post-release success, ensuring that we are consistently building the right things to solve the right problems. RESPONSIBILITIES : Your core responsibilities will follow a structured product management life cycle designed to maximize impact and ensure clarity across the organization. 1. Problem Discovery and Definition (Needs Finding & Articulation) : - Conduct intense "Needs Finding" to get to the bottom of any identified problem, whether it originates from customers, the market, or internal operations like finance. - Utilize various techniques, such as stakeholder interviews and the "five-why framework," to dig deep into a problem's root cause, understand the alternatives available to users, and assess the impact of solving or not solving the problem on their work or their finances. - Develop a deep conviction that the identified problem is the right one to solve, as getting this step right is fundamental to having the highest impact. - Articulate and document the identified needs using presentations, financial models that analyze P&L impact, and other collateral to communicate the problem and its importance effectively. - Convince all relevant stakeholders, including engineering leads, architecture, UX, and quality/regulatory teams, that the problem is worth solving, thereby gaining organizational alignment and building your credibility. 2. Solution Design and Articulation : - Collaborate closely with the engineering lead to create a detailed Implementation Blueprint (the Product Requirements Document or PRD). You must understand how the solution will be implemented and the rationale behind technical decisions. - Articulate the proposed solution to business stakeholders using wireframes, presentations, or other tools to gather early feedback. This "Solution Articulation" is distinct from and must not pollute the "Needs Articulation" phase. - Iterate on the blueprint based on stakeholder feedback until everyone signs off on the proposed solution. - Embrace a "shift left" philosophy, investing significant time in the blueprinting stage to bring in all necessary teams (architecture, UI/UX, QA) to prevent issues downstream. 3. Release Validation and Management : - Develop a comprehensive Validation Plan before the engineering team begins implementation. This plan will serve as a checklist and gating item for the release. - Create and manage a Pre-release Checklist, which includes developing collateral and training materials for internal teams (e.g., customer support, sales) and external users. - For external-facing materials, coordinate with the Brand Marketing team to ensure brand consistency. - Effectively communicate to users why changes are being made and how they can expect to benefit. 4. User Migration and Deployment : - Plan and execute the user migration or deployment process with a focus on minimizing risk. - Design and implement a gradual release strategy (e.g., rolling out to 1%, then 5%, then 20% of users) to identify and fix issues before they impact the entire user base. - Ensure that the product's architecture supports phased rollouts and includes a mechanism to roll back the feature if necessary. 5. Post-Release Impact Analysis : - Conduct post-market surveillance to measure the impact of released features and gather user feedback. - Establish methods for data collection, such as user surveys, questionnaires, or in-app instrumentation, to understand feature adoption and user behavior. - Use the data and evidence collected to create a feedback loop that informs future "Needs Finding" and strengthens your arguments in "Needs Articulation". SKILLS AND QUALIFICATIONS : - Exceptional Communication and Influence : Demonstrated ability to act as a bridge between commercial and technical teams. You must be able to articulate complex needs and solutions clearly and convincingly to diverse audiences. - Deep Investigative and Analytical Skills : A natural curiosity and the ability to perform rigorous "Needs Finding" to uncover the true, underlying problem that needs to be solved. - Strategic and Financial Acumen : The ability to connect product features to larger business objectives and model their financial outcomes. This includes understanding financial statements like a Profit and Loss (P&L) statement to assess how product changes affect revenue and costs, and working with various teams to improve financial performance over time, thereby enhancing the product's sale-ability and its credibility. - Strong Documentation Skills : The ability to write clear, precise, and cogent documents, including PRDs, validation plans, and user-facing collateral. - Collaborative Mindset : A proven track record of working effectively with engineering, design, and business counterparts to bring ideas to life. - Process-Oriented : A commitment to following a systematic product development process while knowing when and how to justify deviations. - Resilience and Drive for Impact : Success in this role is measured by your impact. You must be resilient, especially when facing initial skepticism, and be driven to build credibility through successful product outcomes.

Tricog Health - Product Manager 

Description : We are looking for a curious and passionate Machine Learning Engineer to join our high-impact team. Youll work directly on ML models that analyze cardiac data, helping doctors save lives. This role offers the unique opportunity to see your work make a tangible difference in patient outcomes while building state-of-the-art ML infrastructure. What Youll Do : - Design and Implement ML Models : Develop, train, and evaluate machine learning and deep learning models for cardiac datasets and associated metadata. - Data Pipeline Development : Work with large, complex, and sometimes messy clinical datasets. Contribute to building robust and scalable data pipelines for data ingestion, cleaning, feature engineering, and labeling. - Model Deployment and MLOps : Deploy models to production environments and monitor their performance in real-world clinical settings. - Build and maintain REST APIs for model inference using frameworks like FastAPI or Flask. Design scalable API endpoints with proper request validation, error handling, and authentication. - Implement and maintain robust MLOps practices, including version control, continuous integration/continuous deployment (CI/CD), and model monitoring in a production environment (e.g., cloud platforms like AWS, Azure, or GCP). - Performance Optimization : Optimize model performance for inference speed and resource efficiency, crucial for deployment on various platforms (cloud, edge devices). - Collaboration : Work collaboratively with software engineers, data scientists, and clinical domain experts to translate clinical needs into technical requirements and deliver high-impact solutions. - Documentation and Research : Maintain detailed documentation of models, code, and experiments. Stay current with the latest research in deep learning, medical image analysis, and time-series analysis. - Regulatory and Compliance : Develop documentation in order to comply with regulatory requirements such as CDSCO, FDA etc. Required : - Experience : 1-3 years of professional experience as a Machine Learning Engineer, Data Scientist, or a related role. - Programming : Proficiency in Python and experience with core data science libraries (NumPy, pandas, scikit-learn). - Deep Learning Frameworks : Hands-on experience with at least one major deep learning framework (PyTorch). - MLOps Basics : Familiarity with MLOps principles, including containerization (Docker) and cloud service experience (AWS, Azure, or GCP). - Understanding of model evaluation metrics, cross-validation, and debugging ML systems. - Ability to read and implement research papers. Preferred Skills : - Experience with healthcare data. - Understanding of statistical methods and experimental design for model validation. - Experience with structured training pipelines such as Pytorch Lightning. - Knowledge of regulatory requirements for medical devices (FDA, CE marking). - Experience with cloud platforms (AWS, GCP, Azure) and serverless deployments. - Publications or contributions to ML open-source projects.

Tricog Health - Machine Learning Engineer 

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