Software Architect/CTO

Job Title -Data Scientist

Job Duties

1. Data Scientist responsibilities includes planning projects and building analytics models.
2. You should have a strong problem-solving ability and a knack for statistical analysis.
3. If you're also able to align our data products with our business goals, we'd like to meet you. Your ultimate goal will be to help improve our products and business decisions by making the most out of our data.

Responsibilities

Own end-to-end business problems and metrics, build and implement ML solutions using cutting-edge technology.

Create scalable solutions to business problems using statistical techniques, machine learning, and NLP.

Design, experiment and evaluate highly innovative models for predictive learning

Work closely with software engineering teams to drive real-time model experiments, implementations, and new feature creations

Establish scalable, efficient, and automated processes for large-scale data analysis, model development, deployment, experimentation, and evaluation.

Research and implement novel machine learning and statistical approaches.

Requirements

2-5 years of experience in data science.

In-depth understanding of modern machine learning techniques and their mathematical underpinnings.

Demonstrated ability to build PoCs for complex, ambiguous problems and scale them up.

Strong programming skills (Python, Java)

High proficiency in at least one of the following broad areas: machine learning, statistical modelling/inference, information retrieval, data mining, NLP

Experience with SQL and NoSQL databases

Strong organizational and leadership skills

Excellent communication skills

The Client is the world’s largest media investment company. Our team of experts support clients in programmatic, social, paid search, analytics, technology, organic search, affiliate marketing, e-commerce and across traditional channels.

Responsibilities of the role:

·      Manage extraction of data sets from multiple marketing/database platforms and perform hygiene and quality control steps, either via Datorama or in partnership with Neo Technology team. Data sources will include: web analytic tools, media analytics, customer databases, social listening tools, search tools, syndicated data, research & survey tools, etc…

· Implement and manage data system architecture

· Audit and manage data taxonomy/classifications from multiple systems and partners

· Manage the extraction, loading, and transformation of multiple data sets

· Cleanse all data and metrics; perform override updates where necessary

· Execute all business rules according to requirements · Identify and implement opportunities for efficiency throughout the process

· Manage and execute thorough QA process and ensure quality and accuracy of all data and reporting deliverables

· Manipulate and analyze “big” data sets synthesized from a variety of sources, including media platforms and marketing automation tools

· Generate and manage all data visualizations and ensure data is presented accurately and is visually pleasing

· Assist analytics team in running numerous insights reports as needed

· Help maintain a performance platform and provide insights and ongoing recommendations around.

Requirements :

· 5+ years’ experience in an analytics position working with large amounts of data

· Hands-on experience working with data visualization tools such as Datorama, Tableau, or PowerBI

· Additional desirable skills include tag management experience, application coding experience, statistics background

· Digital media experience background preferred, including knowledge of Doubleclick and web analytics tools

· Excellent communication skills

· Experience with HTML, CSS, Javascript a plus

5-7 years of experience in Data Engineering with solid experience in design, development and implementation of end-to-end data ingestion and data processing system in AWS platform.

2-3 years of experience in AWS Glue, Lambda, Appflow, EventBridge, Python, PySpark, Lake House, S3, Redshift, Postgres, API Gateway, CloudFormation, Kinesis, Athena, KMS, IAM.

Experience in modern data architecture, Lake House, Enterprise Data Lake, Data Warehouse, API interfaces, solution patterns, standards and optimizing data ingestion.

Experience in build of data pipelines from source systems like SAP Concur, Veeva Vault, Azure Cost, various social media platforms or similar source systems.

Expertise in analyzing source data and designing a robust and scalable data ingestion framework and pipelines adhering to client Enterprise Data Architecture guidelines.

Proficient in design and development of solutions for real-time (or near real time) stream data processing as well as batch processing on the AWS platform.

Work closely with business analysts, data architects, data engineers, and data analysts to ensure that the data ingestion solutions meet the needs of the business.

Troubleshoot and provide support for issues related to data quality and data ingestion solutions. This may involve debugging data pipeline processes, optimizing queries, or troubleshooting application performance issues.

Experience in working in Agile/Scrum methodologies, CI/CD tools and practices, coding standards, code reviews, source management (GITHUB), JIRA, JIRA Xray and Confluence.

Experience or exposure to design and development using Full Stack tools.

Strong analytical and problem-solving skills, excellent communication (written and oral), and interpersonal skills.

Bachelor's or master's degree in computer science or related field.

Technical-Requirements: 

• Bachelor's Degree in Computer Science or a related technical field, and solid years of relevant experience. 
• A strong grasp of SQL/Presto and at least one scripting (Python, preferable) or programming language. 
• Experience with an enterprise class BI tools and it's auditing along with automations using REST API's. 
• Experience with reporting tools – QuickSight (preferred, at least 2 years hands on). 
• Tableau/Looker (both or anyone would suffice with at least 5+ years of hands on). 
• 5+ years of experience with and detailed knowledge of data warehouse technical architectures, data modelling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding. 
• 5+ years of demonstrated quantitative and qualitative business intelligence. 
• Experience with significant product analysis based business impact. 
• 4+ years of large IT project delivery for BI oriented projects using agile framework. 
• 2+ years of working with very large data warehousing environment. 
• Experience in designing and delivering cross functional custom reporting solutions. 
• Excellent oral and written communication skills including the ability to communicate effectively with both technical and non-technical stakeholders. 
• Proven ability to meet tight deadlines, multi-task, and prioritize workload. 
• A work ethic based on a strong desire to exceed expectations. 
• Strong analytical and challenge process skills.

Job Summary
SQL development for our Enterprise Resource Planning (ERP) Product offered to SMEs. Regular modifications , creation and validation with testing of stored procedures , views, functions on MS SQL Server.
Responsibilities and Duties
Understanding the ERP Software and use cases.
Regular Creation,modifications and testing of

• Stored Procedures
• Views
• Functions
• Nested Queries
• Table and Schema Designs

Qualifications and Skills
MS SQL

• Procedural Language
• Datatypes
• Objects
• Databases
• Schema

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