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Data Science
Machine Learning (ML)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
Data ScienceMachine Learning (ML)
Deep LearningJob 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
Machine Learning (ML)Data Science- Lead the data science, ML, product analytics, and insights functions by translating sparse and decentralized datasets to develop metrics, standardize processes, and lead the path from data to insights.
- Building visualizations, models, pipelines, alerts/insights systems, and recommendations in Python/Java to support business decisions and operational experiences.
- Advising executives on calibration strategy, DEI, and workforce planning.
Data ScienceMachine Learning (ML)Job Description
Data scientist with strong background in data mining, machine learning, recommendation systems, and statistics. Should possess signature strengths of a qualified mathematician with ability to apply concepts of Mathematics, Applied Statistics, with specialization in one or more of NLP, Computer Vision, Speech, Data mining to develop models that provide effective solution.. A strong data engineering background with hands-on coding capabilities is needed to own and deliver outcomes.
A Master’s or PhD Degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent experience, 7+ years of industry experience in predictive modelling, data science and analysis, with prior experience in a ML or data scientist role and a track record of building ML or DL models.
Responsibilities and skills:
● Work with our customers to deliver a ML / DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization.
● Selecting features, building and optimizing classifiers using ML techniques ● Data mining using state-of-the-art methods, create text mining pipelines to clean & process large unstructured datasets to reveal high quality information and hidden insights using machine learning techniques
● Should be able to appreciate and work on Computer Vision problems – for example extract rich information from images to categorize and process visual data— Develop machine learning algorithms for object and image classification, Experience in using DBScan, PCA, Random Forests and Multinomial Logistic Regression to select the best features to classify objects.
OR
● Deep understanding of NLP such as fundamentals of information retrieval, deep learning approaches, transformers, attention models, text summarisation, attribute extraction, etc. Preferable experience in one or more of the following areas: recommender systems, moderation of user generated content, sentiment analysis, etc.
OR
● Speech recognition, speech to text and vice versa, understanding NLP and IR, text summarisation, statistical and deep learning approaches to text processing. Experience of having worked in these areas.
Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc. Needs to appreciate deep learning frameworks like MXNet, Caffe 2, Keras, Tensorflow
● Experience in working with GPUs to develop models, handling terabyte size datasets ● Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, mlr, mllib, Scikit-learn, caret etc - excellence in at least one of these is highly desirable ● Should be able to work hands-on in Python, R etc. Should closely collaborate & work with engineering teams to iteratively analyse data using Scala, Spark, Hadoop, Kafka, Storm etc.,
● Experience with NoSQL databases and familiarity with data visualization tools will be of great advantage
Data ScienceMachine Learning (ML)We are looking for Data Scientists with product exposure.
Work on Technical Machine Learning, AI and Software Projects
Capture use case and stakeholder requirements, document and in review with architecture team ensure requirements align to relevant features of a release.
Capture a quantification of benefit each feature would bring to stakeholder and how adopting the product would materialize this.
Leveraging the articulation of benefit and impact of features develop a prioritization for next iteration of the product.
Develop the requirements and usage into effective user journeys which aligns to the AI / ML lifecycle.
Carry out industrial research on topic related to product and product families to ensure requirements and propose release align to industry best practices.
Support the documentation generation process to ensure end users will have sufficient information a the correct level of detail to leverage the product.
Support the UAT and testing stages in line with requirements captured by end users.
Interlock with end users to evaluate the effectiveness of the solution and if the pain points / requirements have been addressed
Data ScienceMachine Learning (ML)Location: Ahmedabad / Pune
Team: Technology
Company Profile
InFoCusp is a company working in the broad field of Computer Science, Software Engineering, and Artificial Intelligence (AI). It is headquartered in Ahmedabad, India, having a branch office in Pune.
We have worked on / are working on AI projects / algorithms-heavy projects with applications ranging in finance, healthcare, e-commerce, legal, HR/recruiting, pharmaceutical, leisure sports and computer gaming domains. All of this is based on the core concepts of data science,
computer vision, machine learning (with emphasis on deep learning), cloud computing, biomedical signal processing, text and natural language processing, distributed systems, embedded systems and the Internet of Things.
PRIMARY RESPONSIBILITIES:
● Applying machine learning, deep learning, and signal processing on large datasets (Audio, sensors, images, videos, text) to develop models.
● Architecting large scale data analytics/modeling systems.
● Designing and programming machine learning methods and integrating them into our ML framework/pipeline.
● Analyzing data collected from various sources,
● Evaluate and validate the analysis with statistical methods. Also presenting this in a lucid form to people not familiar with the domain of data science/computer science.
● Writing specifications for algorithms, reports on data analysis, and documentation of algorithms.
● Evaluating new machine learning methods and adapting them for our
purposes.
● Feature engineering to add new features that improve model
performance.
KNOWLEDGE AND SKILL REQUIREMENTS:
● Background and knowledge of recent advances in machine learning, deep learning, natural language processing, and/or image/signal/video processing with at least 3 years of professional work experience working on real-world data.
● Strong programming background, e.g. Python, C/C++, R, Java, and knowledge of software engineering concepts (OOP, design patterns).
● Knowledge of machine learning libraries Tensorflow, Jax, Keras, scikit-learn, pyTorch. Excellent mathematical skills and background, e.g. accuracy, significance tests, visualization, advanced probability concepts
● Ability to perform both independent and collaborative research.
● Excellent written and spoken communication skills.
● A proven ability to work in a cross-discipline environment in defined time frames. Knowledge and experience of deploying large-scale systems using distributed and cloud-based systems (Hadoop, Spark, Amazon EC2, Dataflow) is a big plus.
● Knowledge of systems engineering is a big plus.
● Some experience in project management and mentoring is also a big plus.
EDUCATION:
- B.E.\B. Tech\B.S. candidates' entries with significant prior experience in the aforementioned fields will be considered.
- M.E.\M.S.\M. Tech\PhD preferably in fields related to Computer Science with experience in machine learning, image and signal processing, or statistics preferred.
Data ScienceMachine Learning (ML)XressBees – a logistics company started in 2015 – is amongst the fastest growing companies of its sector. Our
vision to evolve into a strong full-service logistics organization reflects itself in the various lines of business like B2C
logistics 3PL, B2B Xpress, Hyperlocal and Cross border Logistics.
Our strong domain expertise and constant focus on innovation has helped us rapidly evolve as the most trusted
logistics partner of India. XB has progressively carved our way towards best-in-class technology platforms, an
extensive logistics network reach, and a seamless last mile management system.
While on this aggressive growth path, we seek to become the one-stop-shop for end-to-end logistics solutions. Our
big focus areas for the very near future include strengthening our presence as service providers of choice and
leveraging the power of technology to drive supply chain efficiencies.
Job Overview
XpressBees would enrich and scale its end-to-end logistics solutions at a high pace. This is a great opportunity to join
the team working on forming and delivering the operational strategy behind Artificial Intelligence / Machine Learning
and Data Engineering, leading projects and teams of AI Engineers collaborating with Data Scientists. In your role, you
will build high performance AI/ML solutions using groundbreaking AI/ML and BigData technologies. You will need to
understand business requirements and convert them to a solvable data science problem statement. You will be
involved in end to end AI/ML projects, starting from smaller scale POCs all the way to full scale ML pipelines in
production.
Seasoned AI/ML Engineers would own the implementation and productionzation of cutting-edge AI driven algorithmic
components for search, recommendation and insights to improve the efficiencies of the logistics supply chain and
serve the customer better.
You will apply innovative ML tools and concepts to deliver value to our teams and customers and make an impact to
the organization while solving challenging problems in the areas of AI, ML , Data Analytics and Computer Science.
Opportunities for application:
- Route Optimization
- Address / Geo-Coding Engine
- Anomaly detection, Computer Vision (e.g. loading / unloading)
- Fraud Detection (fake delivery attempts)
- Promise Recommendation Engine etc.
- Customer & Tech support solutions, e.g. chat bots.
- Breach detection / prediction
An Artificial Intelligence Engineer would apply himself/herself in the areas of -
- Deep Learning, NLP, Reinforcement Learning
- Machine Learning - Logistic Regression, Decision Trees, Random Forests, XGBoost, etc..
- Driving Optimization via LPs, MILPs, Stochastic Programs, and MDPs
- Operations Research, Supply Chain Optimization, and Data Analytics/Visualization
- Computer Vision and OCR technologies
The AI Engineering team enables internal teams to add AI capabilities to their Apps and Workflows easily via APIs
without needing to build AI expertise in each team – Decision Support, NLP, Computer Vision, for Public Clouds and
Enterprise in NLU, Vision and Conversational AI.Candidate is adept at working with large data sets to find
opportunities for product and process optimization and using models to test the effectiveness of different courses of
action. They must have knowledge using a variety of data mining/data analysis methods, using a variety of data tools,
building, and implementing models, using/creating algorithms, and creating/running simulations. They must be
comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion
for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.
Roles & Responsibilities
● Develop scalable infrastructure, including microservices and backend, that automates training and
deployment of ML models.
● Building cloud services in Decision Support (Anomaly Detection, Time series forecasting, Fraud detection,
Risk prevention, Predictive analytics), computer vision, natural language processing (NLP) and speech that
work out of the box.
● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively
communicating your needs and understanding theirs and address external and internal shareholder's
product challenges.
● Build core of Artificial Intelligence and AI Services such as Decision Support, Vision, Speech, Text, NLP, NLU,
and others.
● Leverage Cloud technology –AWS, GCP, Azure
● Experiment with ML models in Python using machine learning libraries (Pytorch, Tensorflow), Big Data,
Hadoop, HBase, Spark, etc
● Work with stakeholders throughout the organization to identify opportunities for leveraging company data to
drive business solutions.
● Mine and analyze data from company databases to drive optimization and improvement of product
development, marketing techniques and business strategies.
● Assess the effectiveness and accuracy of new data sources and data gathering techniques.
● Develop custom data models and algorithms to apply to data sets.
● Use predictive modeling to increase and optimize customer experiences, supply chain metric and other
business outcomes.
● Develop company A/B testing framework and test model quality.
● Coordinate with different functional teams to implement models and monitor outcomes.
● Develop processes and tools to monitor and analyze model performance and data accuracy.
● Develop scalable infrastructure, including microservices and backend, that automates training and
deployment of ML models.
● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively
communicating your needs and understanding theirs and address external and internal shareholder's
product challenges.
● Deliver machine learning and data science projects with data science techniques and associated libraries
such as AI/ ML or equivalent NLP (Natural Language Processing) packages. Such techniques include a good
to phenomenal understanding of statistical models, probabilistic algorithms, classification, clustering, deep
learning or related approaches as it applies to financial applications.
● The role will encourage you to learn a wide array of capabilities, toolsets and architectural patterns for
successful delivery.
What is required of you?
You will get an opportunity to build and operate a suite of massive scale, integrated data/ML platforms in a broadly
distributed, multi-tenant cloud environment.
● B.S., M.S., or Ph.D. in Computer Science, Computer Engineering
● Coding knowledge and experience with several languages: C, C++, Java,JavaScript, etc.
● Experience with building high-performance, resilient, scalable, and well-engineered systems
● Experience in CI/CD and development best practices, instrumentation, logging systems
● Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights
from large data sets.
● Experience working with and creating data architectures.
● Good understanding of various machine learning and natural language processing technologies, such as
classification, information retrieval, clustering, knowledge graph, semi-supervised learning and ranking.
● Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest,
Boosting, Trees, text mining, social network analysis, etc.
● Knowledge on using web services: Redshift, S3, Spark, Digital Ocean, etc.
● Knowledge on creating and using advanced machine learning algorithms and statistics: regression,
simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
● Knowledge on analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Core metrics,
AdWords, Crimson Hexagon, Facebook Insights, etc.
● Knowledge on distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, MySQL, Kafka etc.
● Knowledge on visualizing/presenting data for stakeholders using: Quicksight, Periscope, Business Objects,
D3, ggplot, Tableau etc.
● Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural
networks, etc.) and their real-world advantages/drawbacks.
● Knowledge of advanced statistical techniques and concepts (regression, properties of distributions,
statistical tests, and proper usage, etc.) and experience with applications.
● Experience building data pipelines that prep data for Machine learning and complete feedback loops.
● Knowledge of Machine Learning lifecycle and experience working with data scientists
● Experience with Relational databases and NoSQL databases
● Experience with workflow scheduling / orchestration such as Airflow or Oozie
● Working knowledge of current techniques and approaches in machine learning and statistical or
mathematical models
● Strong Data Engineering & ETL skills to build scalable data pipelines. Exposure to data streaming stack (e.g.
Kafka)
● Relevant experience in fine tuning and optimizing ML (especially Deep Learning) models to bring down
serving latency.
● Exposure to ML model productionzation stack (e.g. MLFlow, Docker)
● Excellent exploratory data analysis skills to slice & dice data at scale using SQL in Redshift/BigQuery.
Data Analytics
Python
R ProgrammingMachine Learning (ML)JOB DESCRIPTION
- 2 to 6 years of experience in imparting technical training/ mentoring
- Must have very strong concepts of Data Analytics
- Must have hands-on and training experience on Python, Advanced Python, R programming, SAS and machine learning
- Must have good knowledge of SQL and Advanced SQL
- Should have basic knowledge of Statistics
- Should be good in Operating systems GNU/Linux, Network fundamentals,
- Must have knowledge on MS office (Excel/ Word/ PowerPoint)
- Self-Motivated and passionate about technology
- Excellent analytical and logical skills and team player
- Must have exceptional Communication Skills/ Presentation Skills
- Good Aptitude skills is preferred
- Exceptional communication skills
Responsibilities:
- Ability to quickly learn any new technology and impart the same to other employees
- Ability to resolve all technical queries of students
- Conduct training sessions and drive the placement driven quality in the training
- Must be able to work independently without the supervision of a senior person
- Participate in reviews/ meetings
Qualification:
- UG: Any Graduate in IT/Computer Science, B.Tech/B.E. – IT/ Computers
- PG: MCA/MS/MSC – Computer Science
- Any Graduate/ Post graduate, provided they are certified in similar courses
ABOUT EDUBRIDGE
EduBridge is an Equal Opportunity employer and we believe in building a meritorious culture where everyone is recognized for their skills and contribution.
Launched in 2009 EduBridge Learning is a workforce development and skilling organization with 50+ training academies in 18 States pan India. The organization has been providing skilled manpower to corporates for over 10 years and is a leader in its space. We have trained over a lakh semi urban & economically underprivileged youth on relevant life skills and industry-specific skills and provided placements in over 500 companies. Our latest product E-ON is committed to complementing our training delivery with an Online training platform, enabling the students to learn anywhere and anytime.
To know more about EduBridge please visit: http://www.edubridgeindia.com/">http://www.edubridgeindia.com/
You can also visit us on https://www.facebook.com/Edubridgelearning/">Facebook , https://www.linkedin.com/company/edubridgelearning/">LinkedIn for our latest initiatives and products
Data ScienceMachine Learning (ML)PythonDeep Learning- 3+ years of experience in Machine Learning
- Bachelors/Masters in Computer Engineering/Science.
- Bachelors/Masters in Engineering/Mathematics/Statistics with sound knowledge of programming and computer concepts.
- 10 and 12th acedemics 70 % & above.
Skills :
- Strong Python/ programming skills
- Good conceptual understanding of Machine Learning/Deep Learning/Natural Language Processing
- Strong verbal and written communication skills.
- Should be able to manage team, meet project deadlines and interface with clients.
- Should be able to work across different domains and quickly ramp up the business processes & flows & translate business problems into the data solutions
R ProgrammingPythonData ScienceRole and Responsibilities
- Execute data mining projects, training and deploying models over a typical duration of 2 -12 months.
- The ideal candidate should be able to innovate, analyze the customer requirement, develop a solution in the time box of the project plan, execute and deploy the solution.
- Integrate the data mining projects embedded data mining applications in the FogHorn platform (on Docker or Android).
Core Qualifications
Candidates must meet ALL of the following qualifications:
- Have analyzed, trained and deployed at least three data mining models in the past. If the candidate did not directly deploy their own models, they will have worked with others who have put their models into production. The models should have been validated as robust over at least an initial time period.
- Three years of industry work experience, developing data mining models which were deployed and used.
- Programming experience in Python is core using data mining related libraries like Scikit-Learn. Other relevant Python mining libraries include NumPy, SciPy and Pandas.
- Data mining algorithm experience in at least 3 algorithms across: prediction (statistical regression, neural nets, deep learning, decision trees, SVM, ensembles), clustering (k-means, DBSCAN or other) or Bayesian networks
Bonus Qualifications
Any of the following extra qualifications will make a candidate more competitive:
- Soft Skills
- Sets expectations, develops project plans and meets expectations.
- Experience adapting technical dialogue to the right level for the audience (i.e. executives) or specific jargon for a given vertical market and job function.
- Technical skills
- Commonly, candidates have a MS or Ph.D. in Computer Science, Math, Statistics or an engineering technical discipline. BS candidates with experience are considered.
- Have managed past models in production over their full life cycle until model replacement is needed. Have developed automated model refreshing on newer data. Have developed frameworks for model automation as a prototype for product.
- Training or experience in Deep Learning, such as TensorFlow, Keras, convolutional neural networks (CNN) or Long Short Term Memory (LSTM) neural network architectures. If you don’t have deep learning experience, we will train you on the job.
- Shrinking deep learning models, optimizing to speed up execution time of scoring or inference.
- OpenCV or other image processing tools or libraries
- Cloud computing: Google Cloud, Amazon AWS or Microsoft Azure. We have integration with Google Cloud and are working on other integrations.
- Decision trees like XGBoost or Random Forests is helpful.
- Complex Event Processing (CEP) or other streaming data as a data source for data mining analysis
- Time series algorithms from ARIMA to LSTM to Digital Signal Processing (DSP).
- Bayesian Networks (BN), a.k.a. Bayesian Belief Networks (BBN) or Graphical Belief Networks (GBN)
- Experience with PMML is of interest (see www.DMG.org).
- Vertical experience in Industrial Internet of Things (IoT) applications:
- Energy: Oil and Gas, Wind Turbines
- Manufacturing: Motors, chemical processes, tools, automotive
- Smart Cities: Elevators, cameras on population or cars, power grid
- Transportation: Cars, truck fleets, trains
About FogHorn Systems
FogHorn is a leading developer of “edge intelligence” software for industrial and commercial IoT application solutions. FogHorn’s Lightning software platform brings the power of advanced analytics and machine learning to the on-premise edge environment enabling a new class of applications for advanced monitoring and diagnostics, machine performance optimization, proactive maintenance and operational intelligence use cases. FogHorn’s technology is ideally suited for OEMs, systems integrators and end customers in manufacturing, power and water, oil and gas, renewable energy, mining, transportation, healthcare, retail, as well as Smart Grid, Smart City, Smart Building and connected vehicle applications.
Press: https://www.foghorn.io/press-room/">https://www.foghorn.io/press-room/
Awards: https://www.foghorn.io/awards-and-recognition/">https://www.foghorn.io/awards-and-recognition/
- 2019 Edge Computing Company of the Year – Compass Intelligence
- 2019 Internet of Things 50: 10 Coolest Industrial IoT Companies – CRN
- 2018 IoT Planforms Leadership Award & Edge Computing Excellence – IoT Evolution World Magazine
- 2018 10 Hot IoT Startups to Watch – Network World. (Gartner estimated 20 billion connected things in use worldwide by 2020)
- 2018 Winner in Artificial Intelligence and Machine Learning – Globe Awards
- 2018 Ten Edge Computing Vendors to Watch – ZDNet & 451 Research
- 2018 The 10 Most Innovative AI Solution Providers – Insights Success
- 2018 The AI 100 – CB Insights
- 2017 Cool Vendor in IoT Edge Computing – Gartner
- 2017 20 Most Promising AI Service Providers – CIO Review
Our Series A round was for $15 million. Our Series B round was for $30 million October 2017. Investors include: Saudi Aramco Energy Ventures, Intel Capital, GE, Dell, Bosch, Honeywell and The Hive.
About the Data Science Solutions team
In 2018, our Data Science Solutions team grew from 4 to 9. We are growing again from 11. We work on revenue generating projects for clients, such as predictive maintenance, time to failure, manufacturing defects. About half of our projects have been related to vision recognition or deep learning. We are not only working on consulting projects but developing vertical solution applications that run on our Lightning platform, with embedded data mining.
Our data scientists like our team because:
- We care about “best practices”
- Have a direct impact on the company’s revenue
- Give or receive mentoring as part of the collaborative process
- Questions and challenging the status quo with data is safe
- Intellectual curiosity balanced with humility
- Present papers or projects in our “Thought Leadership” meeting series, to support continuous learning
Data ScienceMachine Learning (ML)PythonR Programming
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