R Programming Jobs in Pune

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 QualificationsCandidates 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 QualificationsAny 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 SystemsFogHorn 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/ Awards: 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 teamIn 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

The selected would be a part of the inhouse Data Labs team. He/she would be responsible to creation insights-driven decision structure.This will include:ScorecardsStrategiesMISThe verticals included are:RiskMarketingProduct

Major Roles and Responsibilities:● Collaborate with team members to develop new models to be used forclassification problems● Responsible for software profiling, performance tuning and analysis, and othergeneral software engineering tasks● Use independent judgment to take existing data and build new models from it● Collaborate and provide technical guidance● Coming up with new ideas,rapid prototyping and converting prototypes intoscalable products● Conducting experiments to assess the accuracy and recall of languageprocessing modules and to study the effect of such experiences● Improving existing models and create a self learning systems.● Stakeholder management and leadership● Decision making and problem solvingManagerial activites like Team Handling, performance management, Team Building, Work allocation. Key Success Factors● Extensive experience with Hadoop and Machine learning algorithms● Exposure to Deep Learning, Neural Networks, or related fields and a stronginterest and desire to pursue them● Experience in Natural Language Processing, Computer Vision, Machine Learningor Machine Intelligence (Artificial Intelligence)● Passion for solving NLP problems● Experience with specialized tools and project for working with natural languageprocessing● Programming experience in Python● Knowledge of machine learning frameworks like Tensorflow● Experience with software version control systems like Github● Fast learner and be able to work independently as well as in a team environmentwith good written and verbal communication skillsEducation and Experience● B. E. / B. Tech / Masters in Computer Science● Strong in academics and good aptitude● Excellent communication skills with a flair to learn quickly● 6-10 years of relevant experience● Research and implement novel machine learning and statistical approaches● Prior exposure of product development would be an added advantage

• Exposure to Deep Learning, Neural Networks, or related fields and a strong interest and desire to pursue them. • Experience in Natural Language Processing, Computer Vision, Machine Learning or Machine Intelligence (Artificial Intelligence). • Programming experience in Python. • Knowledge of machine learning frameworks like Tensorflow. • Experience with software version control systems like Github. • Understands the concept of Big Data like Hadoop, MongoDB, Apache Spark