Data Scientist

at Foghorn Systems

Data Scientist

Foghorn Systems

Company

Home

Data Scientist

at Foghorn Systems

Posted by Abhishek Vijayvargia

0 - 7 yrs

₹15L - ₹50L / yr

Pune

Skills

R Programming

Python

Data Science

Role 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

Role 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

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.

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

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About Foghorn Systems

Founded :

2015

Type

Size

Stage :

Raised funding

About

Real-Time Edge Intelligence for Industrial

Connect with the team

Abhishek Vijayvargia

Connect

Company social profiles

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at Precily Private Limited

5 recruiters

Posted by Bharath Rao

Bengaluru (Bangalore), NCR (Delhi | Gurgaon | Noida)

3 - 7 yrs

₹4L - ₹25L / yr

Data Science

Artificial Intelligence (AI)

R Programming

Python

Job Description – Data Scientist About Company Profile Precily is a startup headquartered in Noida, IN. Precily is currently working with leading consulting & law firms, research firms & technology companies. Aura (Precily AI) is data-analysis platform for enterprises that increase the efficiency of the workforce by providing AI-based solutions. Responsibilities & Skills Required: The role requires deep knowledge in designing, planning, testing and deploying analytics solutions including the following: • Natural Language Processing (NLP), Neural Networks , Text Clustering, Topic Modelling, Information Extraction, Information Retrieval, Deep learning, Machine learning, cognitive science and analytics. • Proven experience implementing and deploying advanced AI solutions using R/Python. • Apply machine learning algorithms, statistical data analysis, text clustering, summarization, extracting insights from multiple data points. • Excellent understanding of Analytics concepts and methodologies including machine learning (unsupervised and supervised). • Hand on in handling large amounts of structured and unstructured data. • Measure, interpret, and derive learning from results of analysis that will lead to improvements document processing. Skills Required: • Python, R, NLP, NLG, Machine Learning, Deep Learning & Neural Networks • Word Vectorizers • Word Embeddings ( word2vec & GloVe ) • RNN ( CNN vs RNN ) • LSTM & GRU ( LSTM vs GRU ) • Pretrained Embeddings ( Implementation in RNN ) • Unsupervised Learning • Supervised Learning • Deep Neural Networks • Framework : Keras/tensorflow • Keras Embedding Layer output Please reach out to us: careers@precily.com

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