A Bachelor’s degree in data science, statistics, computer science, or a similar field
2+ years industry experience working in a data science role, such as statistics, machine learning,
deep learning, quantitative financial analysis, data engineering or natural language processing
Domain experience in Financial Services (banking, insurance, risk, funds) is preferred
Have and experience and be involved in producing and rapidly delivering minimum viable products,
results focused with ability to prioritize the most impactful deliverables
Strong Applied Statistics capabilities. Including excellent understanding of Machine Learning
techniques and algorithms
Hands on experience preferable in implementing scalable Machine Learning solutions using Python /
Scala / Java on Azure, AWS or Google cloud platform
Experience with storage frameworks like Hadoop, Spark, Kafka etc
Experience in building &deploying unsupervised, semi-supervised, and supervised models and be
knowledgeable in various ML algorithms such as regression models, Tree-based algorithms,
ensemble learning techniques, distance-based ML algorithms etc
Ability to track down complex data quality and data integration issues, evaluate different algorithmic
approaches, and analyse data to solve problems.
Experience in implementing parallel processing and in-memory frameworks such as H2O.ai
- You're proficient in AI/Machine learning latest technologies
- You're proficient in GPT-3 based algorithms
- You have a passion for writing code as well as understanding and crafting the ways systems interact
- You believe in the benefits of agile processes and shipping code often
- You are pragmatic and work to coalesce requirements into reasonable solutions that provide value
- Deploy well-tested, maintainable and scalable software solutions
- Take end-to-end ownership of the technology stack and product
- Collaborate with other engineers to architect scalable technical solutions
- Embrace and improve our standards and processes to reduce friction and unlock efficiency
Current Ecosystem :
ShibaSwap : https://shibaswap.com/#/
Metaverse : https://shib.io/#/
NFTs : https://opensea.io/collection/theshiboshis
Game : Shiba Eternity on iOS and Android
Position: Senior Speech Recognition Engineer
- Experience : 6+ Years
- Salary : As per market standards
- Location: Bangalore
- Work Mode: Only WFO
- Notice Period: immediate to 30 Days of notice
●Set department objectives.
●Hire, promote, motivate, train, mentor and incentivize the team.
●Innovate, Experiment, and Implement new technologies.
● Contribute to the next level of growth for the AI practice.
●Lead and manage the AI team within the global AI practice
●Work closely with data scientists and AI engineers to create and deploy models catering to
●Establish scalable, efficient, automated processes for data analysis, model development,
validation, deployment, serving, and monitoring
●Work closely with data engineering practice to build and deploy end-to-end AI pipelines
including data processing, model training, and model deployment.
●Ability to build and deploy large-scale enterprise-ready solutions for AI.
●Own and deliver end-to-end large, complex projects within the AI practice.
●Support sales and BD process and present to CXO-level client representatives.
●Work with clients to identify new AI opportunities
●Prepare together with the Sales, Solutioning, and Engineering teams to develop and propose
cutting edge AI solutions
●Contribute to building AI proposals, attending Orals, and providing easy to understand
communications on AI
●Ability to manage Client Relationships
●Cooperate and contribute to Global AI programs
●Reviews proposed designs and make recommendations for improvement.
●Contribute to and promote good software engineering practices across the team.
●Knowledge sharing with the team to adopt best practices,
●Actively contribute to and re-use community best practices.
About Our Company:
●We built an end-to-end AI framework to help our clients to accelerate their journey to launch
●We work closely with academic experts and research groups to solve some of the niches
problems in medical imaging, biopharma, life sciences, law firms, retail, and agriculture
●Work environment – we have an environment to create an impact on the client's business and
transform innovative ideas into reality. Even our junior engineers get the opportunity to work
on different product features in complex domains
●Open communication, flat hierarchy, plenty of individual responsibility
Job Title – Data Scientist (Forecasting)
Anicca Data is seeking a Data Scientist (Forecasting) who is motivated to apply his/her/their skill set to solve complex and challenging problems. The focus of the role will center around applying deep learning models to real-world applications. The candidate should have experience in training, testing deep learning architectures. This candidate is expected to work on existing codebases or write an optimized codebase at Anicca Data. The ideal addition to our team is self-motivated, highly organized, and a team player who thrives in a fast-paced environment with the ability to learn quickly and work independently.
Job Location: Remote (for time being) and Bangalore, India (post-COVID crisis)
- At least 3+ years of experience in a Data Scientist role
- Bachelor's/Master’s degree in Computer Science, Engineering, Statistics, Mathematics, or similar quantitative discipline. D. will add merit to the application process
- Experience with large data sets, big data, and analytics
- Exposure to statistical modeling, forecasting, and machine learning. Deep theoretical and practical knowledge of deep learning, machine learning, statistics, probability, time series forecasting
- Training Machine Learning (ML) algorithms in areas of forecasting and prediction
- Experience in developing and deploying machine learning solutions in a cloud environment (AWS, Azure, Google Cloud) for production systems
- Research and enhance existing in-house, open-source models, integrate innovative techniques, or create new algorithms to solve complex business problems
- Experience in translating business needs into problem statements, prototypes, and minimum viable products
- Experience managing complex projects including scoping, requirements gathering, resource estimations, sprint planning, and management of internal and external communication and resources
- Write C++ and Python code along with TensorFlow, PyTorch to build and enhance the platform that is used for training ML models
- Worked on forecasting projects – both classical and ML models
- Experience with training time series forecasting methods like Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) with Neural Networks (NN) models as Feed-forward NN and Nonlinear Autoregressive
- Strong background in forecasting accuracy drivers
- Experience in Advanced Analytics techniques such as regression, classification, and clustering
- Ability to explain complex topics in simple terms, ability to explain use cases and tell stories
About the Role:
As a Speech Engineer you will be working on development of on-device multilingual speech recognition systems.
- Apart from ASR you will be working on solving speech focused research problems like speech enhancement, voice analysis and synthesis etc.
- You will be responsible for building complete pipeline for speech recognition from data preparation to deployment on edge devices.
- Reading, implementing and improving baselines reported in leading research papers will be another key area of your daily life at Saarthi.
- 2-3 year of hands-on experience in speech recognitionbased projects
- Proven experience as a Speech engineer or similar role
- Should have experience of deployment on edge devices
- Candidate should have hands-on experience with open-source tools such as Kaldi, Pytorch-Kaldi and any of the end-to-end ASR tools such as ESPNET or EESEN or DeepSpeech Pytorch
- Prior proven experience in training and deployment of deep learning models on scale
- Strong programming experience in Python,C/C++, etc.
- Working experience with Pytorch and Tensorflow
- Experience contributing to research communities including publications at conferences and/or journals
- Strong communication skills
- Strong analytical and problem-solving skills
2. Build large datasets that will be used to train the models
3. Empirically evaluate related research works
4. Train and evaluate deep learning architectures on multiple large scale datasets
5. Collaborate with the rest of the research team to produce high-quality research
We are looking for an outstanding Big Data Engineer with experience setting up and maintaining Data Warehouse and Data Lakes for an Organization. This role would closely collaborate with the Data Science team and assist the team build and deploy machine learning and deep learning models on big data analytics platforms.
Roles and Responsibilities:
- Develop and maintain scalable data pipelines and build out new integrations and processes required for optimal extraction, transformation, and loading of data from a wide variety of data sources using 'Big Data' technologies.
- Develop programs in Scala and Python as part of data cleaning and processing.
- Assemble large, complex data sets that meet functional / non-functional business requirements and fostering data-driven decision making across the organization.
- Responsible to design and develop distributed, high volume, high velocity multi-threaded event processing systems.
- Implement processes and systems to validate data, monitor data quality, ensuring production data is always accurate and available for key stakeholders and business processes that depend on it.
- Perform root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
- Provide high operational excellence guaranteeing high availability and platform stability.
- Closely collaborate with the Data Science team and assist the team build and deploy machine learning and deep learning models on big data analytics platforms.
- Experience with Big Data pipeline, Big Data analytics, Data warehousing.
- Experience with SQL/No-SQL, schema design and dimensional data modeling.
- Strong understanding of Hadoop Architecture, HDFS ecosystem and eexperience with Big Data technology stack such as HBase, Hadoop, Hive, MapReduce.
- Experience in designing systems that process structured as well as unstructured data at large scale.
- Experience in AWS/Spark/Java/Scala/Python development.
- Should have Strong skills in PySpark (Python & SPARK). Ability to create, manage and manipulate Spark Dataframes. Expertise in Spark query tuning and performance optimization.
- Experience in developing efficient software code/frameworks for multiple use cases leveraging Python and big data technologies.
- Prior exposure to streaming data sources such as Kafka.
- Should have knowledge on Shell Scripting and Python scripting.
- High proficiency in database skills (e.g., Complex SQL), for data preparation, cleaning, and data wrangling/munging, with the ability to write advanced queries and create stored procedures.
- Experience with NoSQL databases such as Cassandra / MongoDB.
- Solid experience in all phases of Software Development Lifecycle - plan, design, develop, test, release, maintain and support, decommission.
- Experience with DevOps tools (GitHub, Travis CI, and JIRA) and methodologies (Lean, Agile, Scrum, Test Driven Development).
- Experience building and deploying applications on on-premise and cloud-based infrastructure.
- Having a good understanding of machine learning landscape and concepts.
Qualifications and Experience:
Engineering and post graduate candidates, preferably in Computer Science, from premier institutions with proven work experience as a Big Data Engineer or a similar role for 3-5 years.
Good to have at least one of the Certifications listed here:
AZ 900 - Azure Fundamentals
DP 200, DP 201, DP 203, AZ 204 - Data Engineering
AZ 400 - Devops Certification
- B.Tech/MTech from tier 1 institution
- 8+years of experience in machine learning techniques like logistic regression, random forest, boosting, trees, neural networks, etc.
- Showcased experience with Python, SQL and proficiency in Scikit Learn, Pandas, NumPy, Keras and TensorFlow/pytorch
- Experience of working with Qlik sense or Tableau is a plus
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