
Lifespark is looking for individuals with a passion for impacting real lives through technology. Lifespark is one of the most promising startups in the Assistive Tech space in India, and has been honoured with several National and International awards. Our mission is to create seamless, persistent and affordable healthcare solutions. If you are someone who is driven to make a real impact in this world, we are your people.
Lifespark is currently building solutions for Parkinson’s Disease, and we are looking for a ML lead to join our growing team. You will be working directly with the founders on high impact problems in the Neurology domain. You will be solving some of the most fundamental and exciting challenges in the industry and will have the ability to see your insights turned into real products every day
Essential experience and requirements:
1. Advanced knowledge in the domains of computer vision, deep learning
2. Solid understand of Statistical / Computational concepts like Hypothesis Testing, Statistical Inference, Design of Experiments and production level ML system design
3. Experienced with proper project workflow
4. Good at collating multiple datasets (potentially from different sources)
5. Good understanding of setting up production level data pipelines
6. Ability to independently develop and deploy ML systems to various platforms (local and cloud)
7. Fundamentally strong with time-series data analysis, cleaning, featurization and visualisation
8. Fundamental understanding of model and system explainability
9. Proactive at constantly unlearning and relearning
10. Documentation ninja - can understand others documentation as well as create good documentation
Responsibilities :
1. Develop and deploy ML based systems built upon healthcare data in the Neurological domain
2. Maintain deployed systems and upgrade them through online learning
3. Develop and deploy advanced online data pipelines

About Lifespark Technologies
About
Lifespark Technologies is a healthcare technology company. We build solutions for chronic neurological conditions such as Parkinson's Disease, stroke rehabilitation, etc. Our solutions span the domains of AI/ML, medical devices, mobile applications and web applications.
Lifespark aims to create the basic intelligence that will one day solve health issues at the earliest stage before they have damaging results. Your regular clothes will have integrated Lifespark solutions that will tell you the moment you are exposed to any environmental stimulus that is unsuitable for you personally even if it does not affect others. One day, a 60-year-old will look like today's 30-year-old.
The team at Lifespark is driven by one goal: To create things that re-enable those affected today, and keep their own future selves healthy. If that sounds like you, shout out to us and let's make this happen
Lifespark is incubated with IIT Bombay and is the recipient of several prestigious national awards such as the National Bio-Entrepreneurship Competition, Prosus SICA award and DST-BIRAC awards. We're among the top health tech startups in the country and count many prominent business leaders and academics as our mentors. We have deployed our solutions in partnership with some of the largest healthcare organizations in India and are looking to establish ourselves as the go-to innovator globally
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along with metrics to track their progress
Managing available resources such as hardware, data, and personnel so that deadlines
are met
Analysing the ML algorithms that could be used to solve a given problem and ranking
them by their success probability
Exploring and visualizing data to gain an understanding of it, then identifying
differences in data distribution that could affect performance when deploying the model
in the real world
Verifying data quality, and/or ensuring it via data cleaning
Supervising the data acquisition process if more data is needed
Defining validation strategies
Defining the pre-processing or feature engineering to be done on a given dataset
Defining data augmentation pipelines
Training models and tuning their hyper parameters
Analysing the errors of the model and designing strategies to overcome them
Deploying models to production












