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DA DS Trainer JD:
Key Responsibilities:
Develop and deliver comprehensive training programs on data science topics, including machine learning, artificial intelligence, big data, data mining, and predictive analytics.
Create engaging and interactive training materials, including presentations, hands-on exercises, case studies, and assessments.
Conduct classroom, online, and one-on-one training sessions for students at different skill levels.
Evaluate students' performance and progress, providing feedback and support to help them improve.
Stay up-to-date with the latest trends and best practices in data science and incorporate them into training programs.
Collaborate with other trainers and industry experts to continuously improve training content and methodologies.
Assist in the development of training schedules and manage training logistics.
Provide mentorship and career guidance to students aspiring to build a career in data science.
Qualifications:
Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field; a Master’s degree is a plus.
Proven experience as a Data Science Trainer or a similar role.
In-depth knowledge of data science concepts and hands-on experience with tools like Python, R, TensorFlow, and Hadoop.
Excellent presentation and communication skills.
Strong organizational and time-management abilities.
Ability to simplify complex concepts and deliver them in an understandable manner.
Certification in Data Science (e.g., Microsoft Certified: Azure Data Scientist Associate) is preferred.
Preferred Skills:
Experience with LMS (Learning Management Systems) and e-learning platforms.
Strong analytical and problem-solving skills.
Ability to adapt training methods to different learning styles.
Passion for continuous learning and professional development.
Benefits:
Competitive salary and performance-based incentives.
Professional development opportunities.
Flexible working hours.
Collaborative and innovative work environment.
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B1 – Data Scientist - Kofax Accredited Developers
Requirement – 3
Mandatory –
- Accreditation of Kofax KTA / KTM
- Experience in Kofax Total Agility Development – 2-3 years minimum
- Ability to develop and translate functional requirements to design
- Experience in requirement gathering, analysis, development, testing, documentation, version control, SDLC, Implementation and process orchestration
- Experience in Kofax Customization, writing Custom Workflow Agents, Custom Modules, Release Scripts
- Application development using Kofax and KTM modules
- Good/Advance understanding of Machine Learning /NLP/ Statistics
- Exposure to or understanding of RPA/OCR/Cognitive Capture tools like Appian/UI Path/Automation Anywhere etc
- Excellent communication skills and collaborative attitude
- Work with multiple teams and stakeholders within like Analytics, RPA, Technology and Project management teams
- Good understanding of compliance, data governance and risk control processes
Total Experience – 7-10 Years in BPO/KPO/ ITES/BFSI/Retail/Travel/Utilities/Service Industry
Good to have
- Previous experience of working on Agile & Hybrid delivery environment
- Knowledge of VB.Net, C#( C-Sharp ), SQL Server , Web services
Qualification -
- Masters in Statistics/Mathematics/Economics/Econometrics Or BE/B-Tech, MCA or MBA
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Job Description:
1.Be a hands on problem solver with consultative approach, who can apply Machine Learning & Deep Learning algorithms to solve business challenges
a. Use the knowledge of wide variety of AI/ML techniques and algorithms to find what combinations of these techniques can best solve the problem
b. Improve Model accuracy to deliver greater business impact
c.Estimate business impact due to deployment of model
2.Work with the domain/customer teams to understand business context , data dictionaries and apply relevant Deep Learning solution for the given business challenge
3.Working with tools and scripts for sufficiently pre-processing the data & feature engineering for model development – Python / R / SQL / Cloud data pipelines
4.Design , develop & deploy Deep learning models using Tensorflow / Pytorch
5.Experience in using Deep learning models with text, speech, image and video data
a.Design & Develop NLP models for Text Classification, Custom Entity Recognition, Relationship extraction, Text Summarization, Topic Modeling, Reasoning over Knowledge Graphs, Semantic Search using NLP tools like Spacy and opensource Tensorflow, Pytorch, etc
b.Design and develop Image recognition & video analysis models using Deep learning algorithms and open source tools like OpenCV
c.Knowledge of State of the art Deep learning algorithms
6.Optimize and tune Deep Learnings model for best possible accuracy
7.Use visualization tools/modules to be able to explore and analyze outcomes & for Model validation eg: using Power BI / Tableau
8.Work with application teams, in deploying models on cloud as a service or on-prem
a.Deployment of models in Test / Control framework for tracking
b.Build CI/CD pipelines for ML model deployment
9.Integrating AI&ML models with other applications using REST APIs and other connector technologies
10.Constantly upskill and update with the latest techniques and best practices. Write white papers and create demonstrable assets to summarize the AIML work and its impact.
· Technology/Subject Matter Expertise
- Sufficient expertise in machine learning, mathematical and statistical sciences
- Use of versioning & Collaborative tools like Git / Github
- Good understanding of landscape of AI solutions – cloud, GPU based compute, data security and privacy, API gateways, microservices based architecture, big data ingestion, storage and processing, CUDA Programming
- Develop prototype level ideas into a solution that can scale to industrial grade strength
- Ability to quantify & estimate the impact of ML models.
· Softskills Profile
- Curiosity to think in fresh and unique ways with the intent of breaking new ground.
- Must have the ability to share, explain and “sell” their thoughts, processes, ideas and opinions, even outside their own span of control
- Ability to think ahead, and anticipate the needs for solving the problem will be important
· Ability to communicate key messages effectively, and articulate strong opinions in large forums
· Desirable Experience:
- Keen contributor to open source communities, and communities like Kaggle
- Ability to process Huge amount of Data using Pyspark/Hadoop
- Development & Application of Reinforcement Learning
- Knowledge of Optimization/Genetic Algorithms
- Operationalizing Deep learning model for a customer and understanding nuances of scaling such models in real scenarios
- Optimize and tune deep learning model for best possible accuracy
- Understanding of stream data processing, RPA, edge computing, AR/VR etc
- Appreciation of digital ethics, data privacy will be important
- Experience of working with AI & Cognitive services platforms like Azure ML, IBM Watson, AWS Sagemaker, Google Cloud will all be a big plus
- Experience in platforms like Data robot, Cognitive scale, H2O.AI etc will all be a big plus
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Data Scientist-
We are looking for an experienced Data Scientists to join our engineering team and
help us enhance our mobile application with data. In this role, we're looking for
people who are passionate about developing ML/AI in various domains that solves
enterprise problems. We are keen on hiring someone who loves working in fast paced start-up environment and looking to solve some challenging engineering
problems.
As one of the earliest members in engineering, you will have the flexibility to design
the models and architecture from ground up. As any early-stage start-up, we expect
you to be comfortable wearing various hats, and be proactive contributor in building
something truly remarkable.
Responsibilities
Researches, develops and maintains machine learning and statistical models for
business requirements
Work across the spectrum of statistical modelling including supervised,
unsupervised, & deep learning techniques to apply the right level of solution to
the right problem Coordinate with different functional teams to monitor outcomes and refine/
improve the machine learning models Implements models to uncover patterns and predictions creating business value and innovation
Identify unexplored data opportunities for the business to unlock and maximize
the potential of digital data within the organization
Develop NLP concepts and algorithms to classify and summarize structured/unstructured text data
Qualifications
3+ years of experience solving complex business problems using machine
learning.
Fluency in programming languages such as Python, NLP and Bert, is a must
Strong analytical and critical thinking skills
Experience in building production quality models using state-of-the-art technologies
Familiarity with databases like MySQL, Oracle, SQL Server, NoSQL, etc. is
desirable Ability to collaborate on projects and work independently when required.
Previous experience in Fintech/payments domain is a bonus
You should have Bachelor’s or Master’s degree in Computer Science, Statistics
or Mathematics or another quantitative field from a top tier Institute
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- Your responsibilities:
- Build, improve and extend NLP capabilities
- Research and evaluate different approaches to NLP problems
- Must be able to write code that is well designed, produce deliverable results
- Write code that scales and can be deployed to production
- Fundamentals of statistical methods is a must
- Experience in named entity recognition, POS Tagging, Lemmatization, vector representations of textual data and neural networks - RNN, LSTM
- A solid foundation in Python, data structures, algorithms, and general software development skills.
- Ability to apply machine learning to problems that deal with language
- Engineering ability to build robustly scalable pipelines
- Ability to work in a multi-disciplinary team with a strong product focus
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Experience: 1- 5 Years
Job Location: WFH
No. of Position: Multiple
Qualifications: Ph.D. Must have
Work Timings: 1:30 PM IST to 10:30 PM IST
Functional Area: Data Science
NextGen Invent is currently searching for Data Scientist. This role will directly report to the VP, Data Science in Data Science Practice. The person will work on data science use-cases for the enterprise and must have deep expertise in supervised and unsupervised machine learning, modeling and algorithms with a strong focus on delivering use-cases and solutions at speed and scale to solve business problems.
Job Responsibilities:
- Leverage AI/ML modeling and algorithms to deliver on use cases
- Build modeling solutions at speed and scale to solve business problems
- Develop data science solutions that can be tested and deployed in Agile delivery model
- Implement and scale-up high-availability models and algorithms for various business and corporate functions
- Investigate and create experimental prototypes that work on specific domains and verticals
- Analyze large, complex data sets to reveal underlying patterns, and trends
- Support and enhance existing models to ensure better performance
- Set up and conduct large-scale experiments to test hypotheses and delivery of models
Skills, Knowledge, Experience:
- Must have Ph.D. in an analytical or technical field (e.g. applied mathematics, computer science)
- Strong knowledge of statistical and machine learning methods
- Hands on experience on building models at speed and scale
- Ability to work in a collaborative, transparent style with cross-functional stakeholders across the organization to lead and deliver results
- Strong skills in oral and written communication
- Ability to lead a high-functioning team and develop and train people
- Must have programming experience in SQL, Python and R
- Experience conceiving, implementing and continually improving machine learning projects
- Strong familiarity with higher level trends in artificial intelligence and open-source platforms
- Experience working with AWS, Azure, or similar cloud platform
- Familiarity with visualization techniques and software
- Healthcare experience is a plus
- Experience in Kafka, Chatbot and blockchain is a plus.
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Proactively fetches information from various sources and analyzes it for a better understanding of how the business performs, and to build AI tools that automate certain processes within the company.
Roles & Responsibilities
- Develop novel computer vision/NLP algorithms
- Build large datasets that will be used to train the models
- Empirically evaluate related research works
- Train and evaluate deep learning architectures on multiple large scale datasets
- Collaborate with the rest of the research team to produce high quality research
- Manage a team of 2+ interns
Must-have skills
- 2+years of experience in building deep learning models
- Strong basics around probability and statistics, linear algebra, data structure & algorithms
- Good knowledge of classic ML algorithms (regression, SVM, PCA etc.), deep learning
- Strong programming skills
Nice to have skills
- Familiarity with pytorch
- Knowledge of SOTA techniques in NLP and Vision
Benefits
- High level of responsibility and ownership for a product impacting billions of lives.
- Extremely high-quality talent to work with. Work with a global team between US / India.
- Work from anywhere anytime!
- Best of breed industry benefits packages.
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Responsibilities:
- Identify complex business problems and work towards building analytical solutions in-order to create large business impact.
- Demonstrate leadership through innovation in software and data products from ideation/conception through design, development and ongoing enhancement, leveraging user research techniques, traditional data tools, and techniques from the data science toolkit such as predictive modelling, NLP, statistical analysis, vector space modelling, machine learning etc.
- Collaborate and ideate with cross-functional teams to identify strategic questions for the business that can be solved and champion the effectiveness of utilizing data, analytics, and insights to shape business.
- Contribute to company growth efforts, increasing revenue and supporting other key business outcomes using analytics techniques.
- Focus on driving operational efficiencies by use of data and analytics to impact cost and employee efficiency.
- Baseline current analytics capability, ensure optimum utilization and continued advancement to stay abridge with industry developments.
- Establish self as a strategic partner with stakeholders, focused on full innovation system and fully supportive of initiatives from early stages to activation.
- Review stakeholder objectives and team's recommendations to ensure alignment and understanding.
- Drive analytics thought leadership and effectively contributes towards transformational initiatives.
- Ensure accuracy of data and deliverables of reporting employees with comprehensive policies and processes.
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We’re looking to hire someone to help scale Machine Learning and NLP efforts at Episource. You’ll work with the team that develops the models powering Episource’s product focused on NLP driven medical coding. Some of the problems include improving our ICD code recommendations , clinical named entity recognition and information extraction from clinical notes.
This is a role for highly technical machine learning & data engineers who combine outstanding oral and written communication skills, and the ability to code up prototypes and productionalize using a large range of tools, algorithms, and languages. Most importantly they need to have the ability to autonomously plan and organize their work assignments based on high-level team goals.
You will be responsible for setting an agenda to develop and ship machine learning models that positively impact the business, working with partners across the company including operations and engineering. You will use research results to shape strategy for the company, and help build a foundation of tools and practices used by quantitative staff across the company.
What you will achieve:
-
Define the research vision for data science, and oversee planning, staffing, and prioritization to make sure the team is advancing that roadmap
-
Invest in your team’s skills, tools, and processes to improve their velocity, including working with engineering counterparts to shape the roadmap for machine learning needs
-
Hire, retain, and develop talented and diverse staff through ownership of our data science hiring processes, brand, and functional leadership of data scientists
-
Evangelise machine learning and AI internally and externally, including attending conferences and being a thought leader in the space
-
Partner with the executive team and other business leaders to deliver cross-functional research work and models
Required Skills:
-
Strong background in classical machine learning and machine learning deployments is a must and preferably with 4-8 years of experience
-
Knowledge of deep learning & NLP
-
Hands-on experience in TensorFlow/PyTorch, Scikit-Learn, Python, Apache Spark & Big Data platforms to manipulate large-scale structured and unstructured datasets.
-
Experience with GPU computing is a plus.
-
Professional experience as a data science leader, setting the vision for how to most effectively use data in your organization. This could be through technical leadership with ownership over a research agenda, or developing a team as a personnel manager in a new area at a larger company.
-
Expert-level experience with a wide range of quantitative methods that can be applied to business problems.
-
Evidence you’ve successfully been able to scope, deliver and sell your own research in a way that shifts the agenda of a large organization.
-
Excellent written and verbal communication skills on quantitative topics for a variety of audiences: product managers, designers, engineers, and business leaders.
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Fluent in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation, and modeling
Qualifications
-
Professional experience as a data science leader, setting the vision for how to most effectively use data in your organization
-
Expert-level experience with machine learning that can be applied to business problems
-
Evidence you’ve successfully been able to scope, deliver and sell your own work in a way that shifts the agenda of a large organization
-
Fluent in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation, and modeling
-
Degree in a field that has very applicable use of data science / statistics techniques (e.g. statistics, applied math, computer science, OR a science field with direct statistics application)
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5+ years of industry experience in data science and machine learning, preferably at a software product company
-
3+ years of experience managing data science teams, incl. managing/grooming managers beneath you
-
3+ years of experience partnering with executive staff on data topics
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DataWeave provides Retailers and Brands with “Competitive Intelligence as a Service” that enables them to take key decisions that impact their revenue. Powered by AI, we provide easily consumable and actionable competitive intelligence by aggregating and analyzing billions of publicly available data points on the Web to help businesses develop data-driven strategies and make smarter decisions.
Data Science@DataWeave
We the Data Science team at DataWeave (called Semantics internally) build the core machine learning backend and structured domain knowledge needed to deliver insights through our data products. Our underpinnings are: innovation, business awareness, long term thinking, and pushing the envelope. We are a fast paced labs within the org applying the latest research in Computer Vision, Natural Language Processing, and Deep Learning to hard problems in different domains.
How we work?
It's hard to tell what we love more, problems or solutions! Every day, we choose to address some of the hardest data problems that there are. We are in the business of making sense of messy public data on the web. At serious scale!
What do we offer?
- Some of the most challenging research problems in NLP and Computer Vision. Huge text and image datasets that you can play with!
- Ability to see the impact of your work and the value you're adding to our customers almost immediately.
- Opportunity to work on different problems and explore a wide variety of tools to figure out what really excites you.
- A culture of openness. Fun work environment. A flat hierarchy. Organization wide visibility. Flexible working hours.
- Learning opportunities with courses and tech conferences. Mentorship from seniors in the team.
- Last but not the least, competitive salary packages and fast paced growth opportunities.
Who are we looking for?
The ideal candidate is a strong software developer or a researcher with experience building and shipping production grade data science applications at scale. Such a candidate has keen interest in liaising with the business and product teams to understand a business problem, and translate that into a data science problem. You are also expected to develop capabilities that open up new business productization opportunities.
We are looking for someone with 6+ years of relevant experience working on problems in NLP or Computer Vision with a Master's degree (PhD preferred).
Key problem areas
- Preprocessing and feature extraction noisy and unstructured data -- both text as well as images.
- Keyphrase extraction, sequence labeling, entity relationship mining from texts in different domains.
- Document clustering, attribute tagging, data normalization, classification, summarization, sentiment analysis.
- Image based clustering and classification, segmentation, object detection, extracting text from images, generative models, recommender systems.
- Ensemble approaches for all the above problems using multiple text and image based techniques.
Relevant set of skills
- Have a strong grasp of concepts in computer science, probability and statistics, linear algebra, calculus, optimization, algorithms and complexity.
- Background in one or more of information retrieval, data mining, statistical techniques, natural language processing, and computer vision.
- Excellent coding skills on multiple programming languages with experience building production grade systems. Prior experience with Python is a bonus.
- Experience building and shipping machine learning models that solve real world engineering problems. Prior experience with deep learning is a bonus.
- Experience building robust clustering and classification models on unstructured data (text, images, etc). Experience working with Retail domain data is a bonus.
- Ability to process noisy and unstructured data to enrich it and extract meaningful relationships.
- Experience working with a variety of tools and libraries for machine learning and visualization, including numpy, matplotlib, scikit-learn, Keras, PyTorch, Tensorflow.
- Use the command line like a pro. Be proficient in Git and other essential software development tools.
- Working knowledge of large-scale computational models such as MapReduce and Spark is a bonus.
- Be a self-starter—someone who thrives in fast paced environments with minimal ‘management’.
- It's a huge bonus if you have some personal projects (including open source contributions) that you work on during your spare time. Show off some of your projects you have hosted on GitHub.
Role and responsibilities
- Understand the business problems we are solving. Build data science capability that align with our product strategy.
- Conduct research. Do experiments. Quickly build throw away prototypes to solve problems pertaining to the Retail domain.
- Build robust clustering and classification models in an iterative manner that can be used in production.
- Constantly think scale, think automation. Measure everything. Optimize proactively.
- Take end to end ownership of the projects you are working on. Work with minimal supervision.
- Help scale our delivery, customer success, and data quality teams with constant algorithmic improvements and automation.
- Take initiatives to build new capabilities. Develop business awareness. Explore productization opportunities.
- Be a tech thought leader. Add passion and vibrance to the team. Push the envelope. Be a mentor to junior members of the team.
- Stay on top of latest research in deep learning, NLP, Computer Vision, and other relevant areas.
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