LodgIQ is led by a team of experienced hospitality technology experts, data scientists and product domain experts. Seed funded by Highgate Ventures, a venture capital platform focused on early stage technology investments in the hospitality industry and Trilantic Capital Partners, a global private equity firm, LodgIQ has made a significant investment in advanced machine learning platforms and data science.
Title : Data Scientist
- Apply Data Science and Machine Learning to a REAL-LIFE problem - “Predict Guest Arrivals and Determine Best Prices for Hotels”
- Apply advanced analytics in a BIG Data Environment – AWS, MongoDB, SKLearn
- Help scale up the product in a global offering across 100+ global markets
- Minimum 3 years of experience with advanced data analytic techniques, including data mining, machine learning, statistical analysis, and optimization. Student projects are acceptable.
- At least 1 year of experience with Python / Numpy / Pandas / Scipy/ MatPlotLib / Scikit-Learn
- Experience in working with massive data sets, including structured and unstructured with at least 1 prior engagement involving data gathering, data cleaning, data mining, and data visualization
- Solid grasp over optimization techniques
- Master's or PhD degree in Business Analytics. Data science, Statistics or Mathematics
- Ability to show a track record of solving large, complex problems
Expertise in handling large amount of data through Python or PySpark
Conduct data assessment, perform data quality checks and transform data using SQL
and ETL tools
Experience of deploying ETL / data pipelines and workflows in cloud technologies and
architecture such as Azure and Amazon Web Services will be valued
Comfort with data modelling principles (e.g. database structure, entity relationships, UID
etc.) and software development principles (e.g. modularization, testing, refactoring, etc.)
A thoughtful and comfortable communicator (verbal and written) with the ability to
facilitate discussions and conduct training
Track record of strong problem-solving, requirement gathering, and leading by example
Ability to thrive in a flexible and collaborative environment
Track record of completing projects successfully on time, within budget and as per scope
Bigdata JD :
Data Engineer – SQL, RDBMS, pySpark/Scala, Python, Hive, Hadoop, Unix
Data engineering services required:
- Builddataproducts and processes alongside the core engineering and technology team
- Collaborate with seniordatascientists to curate, wrangle, and prepare data for use in their advanced analytical models
- Integratedatafrom a variety of sources, assuring that they adhere to data quality and accessibility standards
- Modify and improvedataengineering processes to handle ever larger, more complex, and more types of data sources and pipelines
- Use Hadoop architecture and HDFS commands to design and optimizedataqueries at scale
- Evaluate and experiment with noveldataengineering tools and advises information technology leads and partners about new capabilities to determine optimal solutions for particular technical problems or designated use cases
Big data engineering skills:
- Demonstrated ability to perform the engineering necessary to acquire, ingest, cleanse, integrate, and structure massive volumes ofdatafrom multiple sources and systems into enterprise analytics platforms
- Proven ability to design and optimize queries to build scalable, modular, efficientdatapipelines
- Ability to work across structured, semi-structured, and unstructureddata, extracting information and identifying linkages across disparatedata sets
- Proven experience delivering production-readydataengineering solutions, including requirements definition, architecture selection, prototype development, debugging, unit-testing, deployment, support, and maintenance
- Ability to operate with a variety ofdataengineering tools and technologies; vendor agnostic candidates preferred
Domain and industry knowledge:
- Strong collaboration and communication skills to work within and across technology teams and business units
- Demonstrates the curiosity, interpersonal abilities, and organizational skills necessary to serve as a consulting partner, includes the ability to uncover, understand, and assess the needs of various business stakeholders
- Experience with problem discovery, solution design, and insight delivery that involves frequent interaction, education, engagement, and evangelism with senior executives
- Ideal candidate will have extensive experience with the creation and delivery of advanced analytics solutions for healthcare payers or insurance companies, including anomaly detection, provider optimization, studies of sources of fraud, waste, and abuse, and analysis of clinical and economic outcomes of treatment and wellness programs involving medical or pharmacy claimsdata, electronic medical recorddata, or other health data
- Experience with healthcare providers, pharma, or life sciences is a plus
at payments bank
- Proficiency in shell scripting
- Proficiency in automation of tasks
- Proficiency in Pyspark/Python
- Proficiency in writing and understanding of sqoop
- Understanding of CloudEra manager
- Good understanding of RDBMS
- Good understanding of Excel
- Extract and present valuable information from data
- Understand business requirements and generate insights
- Build mathematical models, validate and work with them
- Explain complex topics tailored to the audience
- Validate and follow up on results
- Work with large and complex data sets
- Establish priorities with clear goals and responsibilities to achieve a high level of performance.
- Work in an agile and iterative manner on solving problems
- Evaluate different options proactively and the ability to solve problems in an innovative way. Develop new solutions or combine existing methods to create new approaches.
- Good understanding of Digital & analytics
- Strong communication skills, orally and in writing
As a Data Scientist, you will work in collaboration with our business and engineering people, on creating value from data. Often the work requires solving complex problems by turning vast amounts of data into business insights through advanced analytics, modeling, and machine learning. You have a strong foundation in analytics, mathematical modeling, computer science, and math - coupled with a strong business sense. You proactively fetch information from various sources and analyze it for better understanding of how the business performs. Furthermore, you model and build AI tools that automate certain processes within the company. The solutions produced will be implemented to impact business results.
- Develop an understanding of business obstacles, create solutions based on advanced analytics and draw implications for model development
- Combine, explore, and draw insights from data. Often large and complex data assets from different parts of the business.
- Design and build explorative, predictive- or prescriptive models, utilizing optimization, simulation, and machine learning techniques
- Prototype and pilot new solutions and be a part of the aim of ‘productizing’ those valuable solutions that can have an impact at a global scale
- Guides and coaches other chapter colleagues to help solve data/technical problems at an operational level, and in methodologies to help improve development processes
- Identifies and interprets trends and patterns in complex data sets to enable the business to make data-driven decisions
- Measure the sales effectiveness efforts using data science/app/digital nudges.
- Should be able to work on the clickstream data
- Should be well versed and willing to work hands-on various Machine Learning techniques
- Ability to lead a team of 5-6 members.
- Ability to work with large data sets and present conclusions to key stakeholders.
- Develop a clear understanding of the client’s business issue to inform the best approach to the problem.
- Root-cause analysis
- Define data requirements for creating a model and understand the business problem
- Clean, aggregate, analyze, interpret data and carry out quality analysis of it
- Set up data for predictive/prescriptive analysis
- Development of AI/ML models or statistical/econometric models.
- Working along with the team members
- Looking for insight and creating a presentation to demonstrate these insights
- Supporting development and maintenance of proprietary marketing techniques and other knowledge development projects.
Indium Software is a niche technology solutions company with deep expertise in Digital , QA and Gaming. Indium helps customers in their Digital Transformation journey through a gamut of solutions that enhance business value.
With over 1000+ associates globally, Indium operates through offices in the US, UK and India
Visit www.indiumsoftware.com to know more.
Job Title: Analytics Data Engineer
What will you do:
The Data Engineer must be an expert in SQL development further providing support to the Data and Analytics in database design, data flow and analysis activities. The position of the Data Engineer also plays a key role in the development and deployment of innovative big data platforms for advanced analytics and data processing. The Data Engineer defines and builds the data pipelines that will enable faster, better, data-informed decision-making within the business.
Extensive Experience with SQL and strong ability to process and analyse complex data
The candidate should also have an ability to design, build, and maintain the business’s ETL pipeline and data warehouse The candidate will also demonstrate expertise in data modelling and query performance tuning on SQL Server
Proficiency with analytics experience, especially funnel analysis, and have worked on analytical tools like Mixpanel, Amplitude, Thoughtspot, Google Analytics, and similar tools.
Should work on tools and frameworks required for building efficient and scalable data pipelines
Excellent at communicating and articulating ideas and an ability to influence others as well as drive towards a better solution continuously.
Experience working in python, Hive queries, spark, pysaprk, sparkSQL, presto
- Relate Metrics to product
- Programmatic Thinking
- Edge cases
- Good Communication
- Product functionality understanding
Perks & Benefits:
A dynamic, creative & intelligent team they will make you love being at work.
Autonomous and hands-on role to make an impact you will be joining at an exciting time of growth!
Flexible work hours and Attractive pay package and perks
An inclusive work environment that lets you work in the way that works best for you!
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).
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
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.
- 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
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 [email protected]
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.