Clustering Jobs in Mumbai
We are looking for an experienced engineer to join our data science team, who will help us design, develop, and deploy machine learning models in production. You will develop robust models, prepare their deployment into production in a controlled manner, while providing appropriate means to monitor their performance and stability after deployment.
What You’ll Do will include (But not limited to):
- Preparing datasets needed to train and validate our machine learning models
- Anticipate and build solutions for problems that interrupt availability, performance, and stability in our systems, services, and products at scale.
- Defining and implementing metrics to evaluate the performance of the models, both for computing performance (such as CPU & memory usage) and for ML performance (such as precision, recall, and F1)
- Supporting the deployment of machine learning models on our infrastructure, including containerization, instrumentation, and versioning
- Supporting the whole lifecycle of our machine learning models, including gathering data for retraining, A/B testing, and redeployments
- Developing, testing, and evaluating tools for machine learning models deployment, monitoring, retraining.
- Working closely within a distributed team to analyze and apply innovative solutions over billions of documents
- Supporting solutions ranging from rule-bases, classical ML techniques to the latest deep learning systems.
- Partnering with cross-functional team members to bring large scale data engineering solutions to production
- Communicating your approach and results to a wider audience through presentations
- Demonstrated success with machine learning in a SaaS or Cloud environment, with hands–on knowledge of model creation and deployments in production at scale
- Good knowledge of traditional machine learning methods and neural networks
- Experience with practical machine learning modeling, especially on time-series forecasting, analysis, and causal inference.
- Experience with data mining algorithms and statistical modeling techniques for anomaly detection in time series such as clustering, classification, ARIMA, and decision trees is preferred.
- Ability to implement data import, cleansing and transformation functions at scale
- Fluency in Docker, Kubernetes
- Working knowledge of relational and dimensional data models with appropriate visualization techniques such as PCA.
- Solid English skills to effectively communicate with other team members
Due to the nature of the role, it would be nice if you have also:
- Experience with large datasets and distributed computing, especially with the Google Cloud Platform
- Fluency in at least one deep learning framework: PyTorch, TensorFlow / Keras
- Experience with No–SQL and Graph databases
- Experience working in a Colab, Jupyter, or Python notebook environment
- Some experience with monitoring, analysis, and alerting tools like New Relic, Prometheus, and the ELK stack
- Knowledge of Java, Scala or Go-Lang programming languages
- Familiarity with KubeFlow
- Experience with transformers, for example the Hugging Face libraries
- Experience with OpenCV
In a content critical age, Egnyte fuels business growth by enabling content-rich business processes, while also providing organizations with visibility and control over their content assets. Egnyte’s cloud-native content services platform leverages the industry’s leading content intelligence engine to deliver a simple, secure, and vendor-neutral foundation for managing enterprise content across business applications and storage repositories. More than 16,000 customers trust Egnyte to enhance employee productivity, automate data management, and reduce file-sharing cost and complexity. Investors include Google Ventures, Kleiner Perkins, Caufield & Byers, and Goldman Sachs. For more information, visit www.egnyte.com
- You're proficient in AI/Machine learning latest technologies
- You're proficient in GPT-3 based algorithms
- You have a passion for writing code as well as understanding and crafting the ways systems interact
- You believe in the benefits of agile processes and shipping code often
- You are pragmatic and work to coalesce requirements into reasonable solutions that provide value
- Deploy well-tested, maintainable and scalable software solutions
- Take end-to-end ownership of the technology stack and product
- Collaborate with other engineers to architect scalable technical solutions
- Embrace and improve our standards and processes to reduce friction and unlock efficiency
Current Ecosystem :
ShibaSwap : https://shibaswap.com/#/
Metaverse : https://shib.io/#/
NFTs : https://opensea.io/collection/theshiboshis
Game : Shiba Eternity on iOS and Android
Understand business problems and translate business requirements into technical requirements.
Conduct complex data analysis to ensure data quality & reliability i.e., make the data talk by extracting, preparing, and transforming it.
Identify, develop and implement statistical techniques and algorithms to address business challenges and add value to the organization.
Gather requirements and communicate findings in the form of a meaningful story with the stakeholders.
Build & implement data models using predictive modelling techniques. Interact with clients and provide support for queries and delivery
Lead and mentor data analysts.
What we are looking for-
Apart from your love for data and ability to code even while sleeping you would need the following.
Minimum of 02 years of experience in designing and delivery of data science solutions.
You should have successful projects of retail/BFSI/FMCG/Manufacturing/QSR in your kitty to show-off.
Deep understanding of various statistical techniques, mathematical models, and algorithms to start the conversation with the data in hand.
Ability to choose the right model for the data and translate that into a code using R, Python, VBA, SQL, etc.
Bachelors/Masters degree in Engineering/Technology or MBA from
Tier-1 B School or MSc. in Statistics or Mathematics.
- Provide insights based on data to business teams
- Develop framework, solutions and recommendations for business problems
- Build ML models for predictive solutions
- Use advance data science techniques to build business solutions
- Automation / Optimization of new/existing models ensuring smooth,timely and accurate execution with lowest possible TAT.
- Design & maintenance of response tracking, measurement, and comparison of success parameters of various projects.
- Ability to handle large volumes of data with ease using multiple software like Python ,R etc
Experience in modeling techniques and hands on experience in building Logistic regression models, Random Forrest, K-mean Cluster, NLP, Decision tree, Boosting techniques etc
- Good at data interpretation and reasoning skills
- Design thinking to really understand the business problem
- Understanding new ways to deliver (agile, DT)
- Being able to do a functional design across S/4HANA and SCP). An understanding of the possibilities around automation/RPA (which should include UIPath, Blueprism, Contextor) and how these can be identified and embedded in business processes
- Following on from this, the same is true for AI and ML: What is available in SAP standard, how can these be enhanced/developed further, how these technologies can be embedded in the business process. There is no point in understanding the standard process, or the AI and ML components, we will need a new type of hybrid SAP practitioner.
- Handling Survey Scripting Process through the use of survey software platform such as Toluna, QuestionPro, Decipher.
- Mining large & complex data sets using SQL, Hadoop, NoSQL or Spark.
- Delivering complex consumer data analysis through the use of software like R, Python, Excel and etc such as
- Working on Basic Statistical Analysis such as:T-Test &Correlation
- Performing more complex data analysis processes through Machine Learning technique such as:
- Neural Networking
- Creating an Interactive Dashboard Creation through the use of software like Tableau or any other software you are able to use.
- Working on Statistical and mathematical modelling, application of ML and AI algorithms
What you need to have:
- Bachelor or Master's degree in highly quantitative field (CS, machine learning, mathematics, statistics, economics) or equivalent experience.
- An opportunity for one, who is eager of proving his or her data analytical skills with one of the Biggest FMCG market player.
IDfy is ranked amongst the World's Top 100 Regulatory Technology companies for the last two years. IDfy's AI-powered technology solutions help real people unlock real opportunities. We create the confidence required for people and businesses to engage with each other in the digital world. If you have used any major payment wallets, digitally opened a bank account , have used a self-drive car, have played a real-money online game, or hosted people through AirBnB, it's quite likely that your identity has been verified through IDfy at some point.
About the team
- The machine learning team is a closely knit team responsible for building models and services that support key workflows for IDfy.
- Our models are critical for these workflows and as such are expected to perform accurately and with low latency. We use a mix of conventional and hand-crafted deep learning models.
- The team comes from diverse backgrounds and experience. We respect opinions and believe in honest, open communication.
- We work directly with business and product teams to craft solutions for our customers. We know that we are, and function as a platform and not a services company.
About the role
In this role you will:
- Work on all aspects of a production machine learning platform: acquiring data, training and building models, deploying models, building API services for exposing these models, maintaining them in production, and more.
- Work on performance tuning of models
- From time to time work on support and debugging of these production systems
- Work on researching the latest technology in the areas of our interest and applying it to build newer products and enhancement of the existing platform.
- Building workflows for training and production systems
- Contribute to documentation
While the emphasis will be on researching, building and deploying models into production, you will be expected to contribute to aspects mentioned above.
You are a seasoned machine learning engineer (or data scientist). Our ideal candidate is someone with 5+ years of experience in production machine learning.
- You should be experienced in framing and solving complex problems with the application of machine learning or deep learning models.
- Deep expertise in computer vision or NLP with the experience of putting it into production at scale.
- You have experienced that and understand that modelling is only a small part of building and delivering AI solutions and know what it takes to keep a high-performance system up and running.
- Managing a large scale production ML system for at least a couple of years
- Optimization and tuning of models for deployment at scale
- Monitoring and debugging of production ML systems
- An enthusiasm and drive to learn, assimilate and disseminate the state of the art research. A lot of what we are building will require innovative approaches using newly researched models and applications.
- Past experience of mentoring junior colleagues
- Knowledge of and experience in ML Ops and tooling for efficient machine learning processes
Good to Have
- Our stack also includes languages like Go and Elixir. We would love it if you know any of these or take interest in functional programming.
- We use Docker and Kubernetes for deploying our services, so an understanding of this would be useful to have.
- Experience in using any other platform, frameworks, tools.
Other things to keep in mind
- Our goal is to help a significant part of the world’s population unlock real opportunities. This is an opportunity to make a positive impact here, and we hope you like it as much as we do.
Life At IDfy
People at IDfy care about creating value. We take pride in the strong collaborative culture that we have built, and our love for solving challenging problems. Life at IDfy is not always what you’d expect at a tech start-up that’s growing exponentially every quarter. There’s still time and space for balance.
We host regular talks, events and performances around Life, Art, Sports, and Technology; continuously sparking creative neurons in our people to keep their intellectual juices flowing. There’s never a dull day at IDfy. The office environment is casual and it goes beyond just the dress code. We have no conventional hierarchies and believe in an open-door policy where everyone is approachable.
Square Panda is a startup headquartered in Sunnyvale, CA with additional offices located in India
and China. We are in a research based Ed-Tech space, focusing on children's early literacy. We
have 3000+ schools under our belt and are proud to cater the needs of English Language
development of 70,000+ kids worldwide. Our multisensory neuroscience research-based phonics
learning system comes equipped with educational games to teach children many essential skills.
With a curriculum that has been specially adapted for Indian schools and children, we strive to
empower beginner learners through phonics awareness.
o Lead the identification and execution of opportunities where Analytics can make a difference across the company with focus on multiple markets we operate in - US, India and China.
o Actively champion adoption of the analytics solutions across various markets and functions
o Translate business problems into Insights projects and lead in quantifying the various types of risk and rewards that allow these projects to be prioritized.
o Excellent project management and executive communication skills.
o Understanding of the techniques and technologies of data science, along with a detailed understanding of the challenges associated with each (e.g. overfitting, model refresh, challenge of acquiring training data, cost of compute, etc.)
o Proactively and continuously assess the marketplace and its dynamics, customers, and competitors.
o Provide an unbiased point of view on the performance of markets/brands, supported by facts and evidence
o Develop research strategies to ensure internal understanding of customer and competitor insights
o Develop and implement market research & analytical plans, in collaboration with cross‐functional teams.
o Seeks out alternative/creative ways of meeting an information need, considering new techniques to address business challenges.
o Monitor program risks and ensure appropriate actions are taken, including escalating issues timely to the management
o Maintain understanding of business operations and how users interact with the relevant systems and use that understanding to provide decision support analysis.
o Tap the working knowledge of AI and analytics to convey these business goals to the data professionals who will create the models and solutions.
o Enthusiasm, commitment, and business savvy to navigate the technical, political, and organizational roadblocks that can emerge.
o 6-8 years of experience in executing multiple analytics projects end to end
o Entrepreneurial mind-set o Should have played hands on data sciences role in the past with full knowledge of which analytics technique to apply
o Background of core consulting / start up handling end to end projects o Excellent communication and project management skills o Ability to lead both business and analytics team to generate ROI and success for business o Expertise across the spectrum of analytics - Dashboards, Visualizations, Insights, Data Science driven AI, ML Projects
Solve problems in speech and NLP domain using advanced Deep learning and Machine Learning techniques. Few examples of the problems are -
* Limited resource Speaker Diarization on mono-channel recordings in noisy environment.
* Speech Enhancement to improve accuracy of downstream speech analytics tasks.
* Automated Speech Recognition for accent heavy audio with a noisy background.
* Speech analytic tasks, which include: emotions, empathy, keyword extraction.
* Text analytic tasks, which include: topic modeling, entity and intent extraction, opinion mining, text classification, and sentiment detection on multilingual data.
A typical day at work
You will work closely with the product team to own a business problem. You will then model the business problem into a Machine Learning problem. Next you will do literature review to identify approaches to solve the problem. Test these approaches, identify the best approach, add your own insights to improve the performance and ship that to production!
What should you know?
* Solid understanding of Classical Machine Learning and Deep Learning concepts and algorithms.
* Experience with literature review either in academia or industry.
* Proficiency in at least one programming language such as Python, C, C++, Java, etc.
* Proficiency in Machine Learning tools such as TensorFlow, Keras, Caffe, Torch/PyTorch or Theano.
* Advanced degree in Computer Science, Electrical Engineering, Machine Learning, Mathematics, Statistics, Physics, or Computational Linguistics
* You’ll learn insanely fast here.
* Esops and competitive compensation.
* Opportunity and encouragement for publishing research at top conferences, paid trips to attend workshop and conferences where you have published.
* Independent work, flexible timings and sense of ownership of your work.
* Mentorship from distinguished researchers and professors.