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Company Name: Curl Tech
Location: Bangalore
Website: www.curl.tech
Company Profile: Curl Tech is a deep-tech firm, based out of Bengaluru, India. Curl works on developing Products & Solutions leveraging emerging technologies such as Machine Learning, Blockchain (DLT) & IoT. We work on domains such as Commodity Trading, Banking & Financial Services, Healthcare, Logistics & Retail.
Curl has been founded by technology enthusiasts with rich industry experience. Products and solutions that have been developed at Curl, have gone on to have considerable success and have in turn become separate companies (focused on that product / solution).
If you are looking for a job, that would challenge you and desire to work with an organization that disrupts entire value chain; Curl is the right one for you!
Designation: Data Scientist or Junior Data Scientist (according to experience)
Job Description:
Good with Machine Learning and Deep learning, good with programming and maths.
Details: The candidate will be working on many image analytics/ numerical data analytics projects. The work involves, data collection, building the machine learning models, deployment, client interaction and publishing academic papers.
Responsibilities:
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The candidate will be working on many image analytics/numerical data projects.
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Candidate will be building various machine learning models depending upon the requirements.
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Candidate would be responsible for deployment of the machine learning models.
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Candidate would be the face of the company in front of the clients and will have regular client interactions to understand that client requirements.
What we are looking for candidates with:
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Basic Understanding of Statistics, Time Series, Machine Learning, Deep Learning, and their fundamentals and mathematical underpinnings.
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Proven code proficiency in Python,C/C++ or any other AI language of choice.
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Strong algorithmic thinking, creative problem solving and the ability to take ownership and do independent
research.
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Understanding how things work internally in ML and DL models is a must.
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Understanding of the fundamentals of Computer Vision and Image Processing techniques would be a plus.
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Expertise in OpenCV, ML/Neural networks technologies and frameworks such as PyTorch, Tensorflow would be a
plus.
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Educational background in any quantitative field (Computer Science / Mathematics / Computational Sciences and related disciplines) will be given preference.
Education: BE/ BTech/ B.Sc.(Physics or Mathematics)/Masters in Mathematics, Physics or related branches.
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A Bachelor’s degree in data science, statistics, computer science, or a similar field
2+ years industry experience working in a data science role, such as statistics, machine learning,
deep learning, quantitative financial analysis, data engineering or natural language processing
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Strong Applied Statistics capabilities. Including excellent understanding of Machine Learning
techniques and algorithms
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Roles & Responsibilities:
-Adopt novel and breakthrough Deep Learning/Machine Learning technology to fully solve real world problems for different industries. -Develop prototypes of machine learning models based on existing research papers.
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Must Have:
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. - Thorough understanding of full ML pipeline, starting from data collection to model building to inference.
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- Years with relevant experience:
5+ -Experience or Knowledge in ML OPS.
Good to Have: NLP: Text classification, entity extraction, content summarization. AWS, Docker.
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● 4 to 6 years of strong experience in data mining, machine learning and
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● BS/MS/Ph.D. in Computer Science, Statistics, Applied Math, or related areas
from Premier institutes ( only IITs / IISc / BITS / Top NITs or top US university
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We are looking for a Machine Learning engineer for on of our premium client.
Experience: 2-9 years
Location: Gurgaon/Bangalore
Tech Stack:
Python, PySpark, the Python Scientific Stack; MLFlow, Grafana, Prometheus for machine learning pipeline management and monitoring; SQL, Airflow, Databricks, our own open-source data pipelining framework called Kedro, Dask/RAPIDS; Django, GraphQL and ReactJS for horizontal product development; container technologies such as Docker and Kubernetes, CircleCI/Jenkins for CI/CD, cloud solutions such as AWS, GCP, and Azure as well as Terraform and Cloudformation for deployment
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- Demonstrate ability in NLP/ML/DL project solutions and architectures.
- Strong ability in developing NLP tool and end to end solutions.
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- Hands on experience in building NLP models using different NLP libraries and toolkit like NLTK, Stanford NLP, TextBlob, OCR etc.
- Strong programming skills in Python
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- Strong problem solving, logical and communication skills.
Tags: Natural Language Processing (NLP), Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Toolkit (NLTK), Analytics
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Duties and Responsibilities:
Research and Develop Innovative Use Cases, Solutions and Quantitative Models
Quantitative Models in Video and Image Recognition and Signal Processing for cloudbloom’s
cross-industry business (e.g., Retail, Energy, Industry, Mobility, Smart Life and
Entertainment).
Design, Implement and Demonstrate Proof-of-Concept and Working Proto-types
Provide R&D support to productize research prototypes.
Explore emerging tools, techniques, and technologies, and work with academia for cutting-
edge solutions.
Collaborate with cross-functional teams and eco-system partners for mutual business benefit.
Team Management Skills
Academic Qualification
7+ years of professional hands-on work experience in data science, statistical modelling, data
engineering, and predictive analytics assignments
Mandatory Requirements: Bachelor’s degree with STEM background (Science, Technology,
Engineering and Management) with strong quantitative flavour
Innovative and creative in data analysis, problem solving and presentation of solutions.
Ability to establish effective cross-functional partnerships and relationships at all levels in a
highly collaborative environment
Strong experience in handling multi-national client engagements
Good verbal, writing & presentation skills
Core Expertise
Excellent understanding of basics in mathematics and statistics (such as differential
equations, linear algebra, matrix, combinatorics, probability, Bayesian statistics, eigen
vectors, Markov models, Fourier analysis).
Building data analytics models using Python, ML libraries, Jupyter/Anaconda and Knowledge
database query languages like SQL
Good knowledge of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM,
Decision Forests.
Strong Math Skills (Multivariable Calculus and Linear Algebra) - understanding the
fundamentals of Multivariable Calculus and Linear Algebra is important as they form the basis
of a lot of predictive performance or algorithm optimization techniques.
Deep learning : CNN, neural Network, RNN, tensorflow, pytorch, computervision,
Large-scale data extraction/mining, data cleansing, diagnostics, preparation for Modeling
Good applied statistical skills, including knowledge of statistical tests, distributions,
regression, maximum likelihood estimators, Multivariate techniques & predictive modeling
cluster analysis, discriminant analysis, CHAID, logistic & multiple regression analysis
Experience with Data Visualization Tools like Tableau, Power BI, Qlik Sense that help to
visually encode data
Excellent Communication Skills – it is incredibly important to describe findings to a technical
and non-technical audience
Capability for continuous learning and knowledge acquisition.
Mentor colleagues for growth and success
Strong Software Engineering Background
Hands-on experience with data science tools
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● Proficient in Python and using packages like NLTK, Numpy, Pandas
● Should have worked on deep learning frameworks (like Tensorflow, Keras, PyTorch, etc)
● Hands-on experience in Natural Language Processing, Sequence, and RNN Based models
● Mathematical intuition of ML and DL algorithms
● Should be able to perform thorough model evaluation by creating hypotheses on the basis of statistical
analyses
● Should be comfortable in going through open-source code and reading research papers.
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About Turing:
Turing enables U.S. companies to hire the world’s best remote software engineers. 100+ companies including those backed by Sequoia, Andreessen, Google Ventures, Benchmark, Founders Fund, Kleiner, Lightspeed, and Bessemer have hired Turing engineers. For more than 180,000 engineers across 140 countries, we are the preferred platform for finding remote U.S. software engineering roles. We offer a wide range of full-time remote opportunities for full-stack, backend, frontend, DevOps, mobile, and AI/ML engineers.
We are growing fast (our revenue 15x’d in the past 12 months and is accelerating), and we have raised $14M in seed funding (https://tcrn.ch/3lNKbM9">one of the largest in Silicon Valley) from:
- Facebook’s 1st CTO and Quora’s Co-Founder (Adam D’Angelo)
- Executives from Google, Facebook, Square, Amazon, and Twitter
- Foundation Capital (investors in Uber, Netflix, Chegg, Lending Club, etc.)
- Cyan Banister
- Founder of Upwork (Beerud Sheth)
We also raised a much larger round of funding in October 2020 that we will be publicly announcing over the coming month.
Some articles about Turing:
- https://techcrunch.com/2020/08/25/turing-raises-14m-to-help-source-vet-place-and-manage-remote-developers-in-tech-jobs/">TechCrunch: Turing raises $14M seed to help source, vet, place, and manage remote developers
- https://www.theinformation.com/articles/six-startups-prospering-during-coronavirus">The Information: Six Startups Prospering During Coronavirus
- https://medium.com/@cyanbanister/turing-helps-the-world-level-up-ff44b4e6415d">Cyan Banister: Turing Helps the World Level Up
- https://turing.com/boundarylessblog/2019/10/the-future-of-work-is-remote/the-future-of-work/">Jonathan Siddharth (Turing CEO): The Future of Work is Remote.
Turing is led by successful repeat founders Jonathan Siddharth and Vijay Krishnan, whose last A.I. company leveraged elite remote talent and had a successful acquisition. (https://techcrunch.com/2017/02/23/revcontent-acquires-rover/">Techcrunch story). Turing’s leadership team is composed of ex-Engineering and Sales leadership from Facebook, Google, Uber, and Capgemini.
About the role:
Software developers from all over the world have taken 200,000+ tests and interviews on Turing. Turing has also recommended thousands of developers to its customers and got customer feedback in terms of customer interview pass/fail data and data from the success of the collaboration with a U.S customer. This generates a massive proprietary dataset with a rich feature set comprising resume and test/interview features and labels in the form of actual customer feedback. Continuing rapid growth in our business creates an ever-increasing data advantage for us.
We are looking for a Machine Learning Scientist who can help solve a whole range of exciting and valuable machine learning problems at Turing. Turing collects a lot of valuable heterogeneous signals about software developers including their resume, GitHub profile and associated code and a lot of fine-grained signals from Turing’s own screening tests and interviews (that span various areas including Computer Science fundamentals, project ownership and collaboration, communication skills, proactivity and tech stack skills), their history of successful collaboration with different companies on Turing, etc.
A machine learning scientist at Turing will help create deep developer profiles that are a good representation of a developer’s strengths and weaknesses as it relates to their probability of getting successfully matched to one of Turing’s partner companies and having a fruitful long-term collaboration. The ML scientist will build models that are able to rank developers for different jobs based on their probability of success at the job.
You will also help make Turing’s tests more efficient by assessing their ability to predict the probability of a successful match of a developer with at least one company. The prior probability of a registered developer getting matched with a customer is about 1%. We want our tests to adaptively reduce perplexity as steeply as possible and move this probability estimate rapidly toward either 0% or 100%; maximize expected information-gain per unit time in other words.
As an ML Scientist on the team, you will have a unique opportunity to make an impact by advancing ML models and systems, as well as uncovering new opportunities to apply machine learning concepts to Turing product(s).
This role will directly report to Turing’s founder and CTO, https://www.linkedin.com/in/vijay0/">Vijay Krishnan. This is his https://scholar.google.com/citations?user=uCRc7DgAAAAJ&hl=en">Google Scholar profile.
Responsibilities:
- Enhance our existing machine learning systems using your core coding skills and ML knowledge.
- Take end to end ownership of machine learning systems - from data pipelines, feature engineering, candidate extraction, model training, as well as integration into our production systems.
- Utilize state-of-the-art ML modeling techniques to predict user interactions and the direct impact on the company’s top-line metrics.
- Design features and builds large scale recommendation systems to improve targeting and engagement.
- Identify new opportunities to apply machine learning to different parts of our product(s) to drive value for our customers.
Minimum Requirements:
- BS, MS, or Ph.D. in Computer Science or a relevant technical field (AI/ML preferred).
- Extensive experience building scalable machine learning systems and data-driven products working with cross-functional teams
- Expertise in machine learning fundamentals, applicable to search - Learning to Rank, Deep Learning, Tree-Based Models, Recommendation Systems, Relevance and Data mining, understanding of NLP approaches like W2V or Bert.
- 2+ years of experience applying machine learning methods in settings like recommender systems, search, user modeling, graph representation learning, natural language processing.
- Strong understanding of neural network/deep learning, feature engineering, feature selection, optimization algorithms. Proven ability to dig deep into practical problems and choose the right ML method to solve them.
- Strong programming skills in Python and fluency in data manipulation (SQL, Spark, Pandas) and machine learning (scikit-learn, XGBoost, Keras/Tensorflow) tools.
- Good understanding of mathematical foundations of machine learning algorithms.
- Ability to be available for meetings and communication during Turing's "coordination hours" (Mon - Fri: 8 am to 12 pm PST).
Other Nice-to-have Requirements:
- First author publications in ICML, ICLR, NeurIPS, KDD, SIGIR, and related conferences/journals.
- Strong performance in Kaggle competitions.
- 5+ years of industry experience or a Ph.D. with 3+ years of industry experience in applied machine learning in similar problems e.g. ranking, recommendation, ads, etc.
- Strong communication skills.
- Experienced in leading large-scale multi-engineering projects.
- Flexible, and a positive team player with outstanding interpersonal skills.
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- Strong in problem solving, algorithms and data structures
- Proficient in Python
- Hands on experience in technologies and tools related to any of NLP, Deep learning, Machine learning, Conversational AI
- Experience with knowledge Graph or any graph based system is plus
- Able to train and deploy models
- Broad knowledge of machine learning algorithms and principles
- Performance profiling and Tuning
- Communicate and propose solutions to business challenges
- Familiarity with at least one of the cloud computing infrastructure - GCP/AWS
- Keep abreast with the latest technological advances
- Team mentoring and leadership skills.
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