About Monexo Fintech
Monexo is an Reserve Bank of India approved Peer-to-Peer (P2P) Lending marketplace. P2P lending is democratizing finance.
We are driving 'credit inclusion' through paperless onboarding of young working class earning between 15,000 to 30,000 per month with Data Science.
Being a marketplace model - we have lenders who are diversifying beyond Saving Account, Fixed Deposits and Mutual Funds to earn a better yield on their savings.
We are part of the India FinTech industry and invite you to be part of this revolution.
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Job Description
We are looking for an experienced engineer to join our data science team, who will help us design, develop, and deploy machine learning models in production. You will develop robust models, prepare their deployment into production in a controlled manner, while providing appropriate means to monitor their performance and stability after deployment.
What You’ll Do will include (But not limited to):
- Preparing datasets needed to train and validate our machine learning models
- Anticipate and build solutions for problems that interrupt availability, performance, and stability in our systems, services, and products at scale.
- Defining and implementing metrics to evaluate the performance of the models, both for computing performance (such as CPU & memory usage) and for ML performance (such as precision, recall, and F1)
- Supporting the deployment of machine learning models on our infrastructure, including containerization, instrumentation, and versioning
- Supporting the whole lifecycle of our machine learning models, including gathering data for retraining, A/B testing, and redeployments
- Developing, testing, and evaluating tools for machine learning models deployment, monitoring, retraining.
- Working closely within a distributed team to analyze and apply innovative solutions over billions of documents
- Supporting solutions ranging from rule-bases, classical ML techniques to the latest deep learning systems.
- Partnering with cross-functional team members to bring large scale data engineering solutions to production
- Communicating your approach and results to a wider audience through presentations
Your Qualifications:
- Demonstrated success with machine learning in a SaaS or Cloud environment, with hands–on knowledge of model creation and deployments in production at scale
- Good knowledge of traditional machine learning methods and neural networks
- Experience with practical machine learning modeling, especially on time-series forecasting, analysis, and causal inference.
- Experience with data mining algorithms and statistical modeling techniques for anomaly detection in time series such as clustering, classification, ARIMA, and decision trees is preferred.
- Ability to implement data import, cleansing and transformation functions at scale
- Fluency in Docker, Kubernetes
- Working knowledge of relational and dimensional data models with appropriate visualization techniques such as PCA.
- Solid English skills to effectively communicate with other team members
Due to the nature of the role, it would be nice if you have also:
- Experience with large datasets and distributed computing, especially with the Google Cloud Platform
- Fluency in at least one deep learning framework: PyTorch, TensorFlow / Keras
- Experience with No–SQL and Graph databases
- Experience working in a Colab, Jupyter, or Python notebook environment
- Some experience with monitoring, analysis, and alerting tools like New Relic, Prometheus, and the ELK stack
- Knowledge of Java, Scala or Go-Lang programming languages
- Familiarity with KubeFlow
- Experience with transformers, for example the Hugging Face libraries
- Experience with OpenCV
About Egnyte
In a content critical age, Egnyte fuels business growth by enabling content-rich business processes, while also providing organizations with visibility and control over their content assets. Egnyte’s cloud-native content services platform leverages the industry’s leading content intelligence engine to deliver a simple, secure, and vendor-neutral foundation for managing enterprise content across business applications and storage repositories. More than 16,000 customers trust Egnyte to enhance employee productivity, automate data management, and reduce file-sharing cost and complexity. Investors include Google Ventures, Kleiner Perkins, Caufield & Byers, and Goldman Sachs. For more information, visit www.egnyte.com
#LI-Remote
Seeking Data Analytics Trainer with Power BI and Tableau Expertise
Experience Required: Minimum 3 Years
Location: Indore
Part-Time / Full-Time Availability
We are actively seeking a qualified candidate to join our team as a Data Analytics Trainer, with a strong focus on Power BI and Tableau expertise. The ideal candidate should possess the following qualifications:
A track record of 3 to 6 years in delivering technical training and mentoring.
Profound understanding of Data Analytics concepts.
Strong proficiency in Excel and Advanced Excel.
Demonstrated hands-on experience and effective training skills in Python, Data Visualization, R Programming, and an in-depth understanding of both Power BI and Tableau.
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https://www.linkedin.com/in/shweta-bharti-a105ab197/
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.
Requirements:
- 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
Must Have Skills:
- Solid Knowledge on DWH, ETL and Big Data Concepts
- Excellent SQL Skills (With knowledge of SQL Analytics Functions)
- Working Experience on any ETL tool i.e. SSIS / Informatica
- Working Experience on any Azure or AWS Big Data Tools.
- Experience on Implementing Data Jobs (Batch / Real time Streaming)
- Excellent written and verbal communication skills in English, Self-motivated with strong sense of ownership and Ready to learn new tools and technologies
Preferred Skills:
- Experience on Py-Spark / Spark SQL
- AWS Data Tools (AWS Glue, AWS Athena)
- Azure Data Tools (Azure Databricks, Azure Data Factory)
Other Skills:
- Knowledge about Azure Blob, Azure File Storage, AWS S3, Elastic Search / Redis Search
- Knowledge on domain/function (across pricing, promotions and assortment).
- Implementation Experience on Schema and Data Validator framework (Python / Java / SQL),
- Knowledge on DQS and MDM.
Key Responsibilities:
- Independently work on ETL / DWH / Big data Projects
- Gather and process raw data at scale.
- Design and develop data applications using selected tools and frameworks as required and requested.
- Read, extract, transform, stage and load data to selected tools and frameworks as required and requested.
- Perform tasks such as writing scripts, web scraping, calling APIs, write SQL queries, etc.
- Work closely with the engineering team to integrate your work into our production systems.
- Process unstructured data into a form suitable for analysis.
- Analyse processed data.
- Support business decisions with ad hoc analysis as needed.
- Monitoring data performance and modifying infrastructure as needed.
Responsibility: Smart Resource, having excellent communication skills
Responsibilities
- Understanding the business requirements so as to formulate the problems to solve and restrict the slice of data to be explored.
- Collecting data from various sources.
- Performing cleansing, processing, and validation on the data subject to analyze, in order to ensure its quality.
- Exploring and visualizing data.
- Performing statistical analysis and experiments to derive business insights.
- Clearly communicating the findings from the analysis to turn information into something actionable through reports, dashboards, and/or presentations.
Skills
- Experience solving problems in the project’s business domain.
- Experience with data integration from multiple sources
- Proficiency in at least one query language, especially SQL.
- Working experience with NoSQL databases, such as MongoDB and Elasticsearch.
- Working experience with popular statistical and machine learning techniques, such as clustering, linear regression, KNN, decision trees, etc.
- Good scripting skills using Python, R or any other relevant language
- Proficiency in at least one data visualization tool, such as Matplotlib, Plotly, D3.js, ggplot, etc.
- Great communication skills.
- Strong in problem solving, algorithms and data structures
- Proficient in Python
- Hands on experience in technologies and tools related to any of NLP, Deep learning, Machine learning, Conversational AI
- Experience with knowledge Graph or any graph based system is plus
- Able to train and deploy models
- Broad knowledge of machine learning algorithms and principles
- Performance profiling and Tuning
- Communicate and propose solutions to business challenges
- Familiarity with at least one of the cloud computing infrastructure - GCP/AWS
- Keep abreast with the latest technological advances
- Team mentoring and leadership skills.
Only a solid grounding in computer engineering, Unix, data structures and algorithms would enable you to meet this challenge. 7+ years of experience architecting, developing, releasing, and maintaining large-scale big data platforms on AWS or GCP Understanding of how Big Data tech and NoSQL stores like MongoDB, HBase/HDFS, ElasticSearch synergize to power applications in analytics, AI and knowledge graphs Understandingof how data processing models, data location patterns, disk IO, network IO, shuffling affect large scale text processing - feature extraction, searching etc Expertise with a variety of data processing systems, including streaming, event, and batch (Spark, Hadoop/MapReduce) 5+ years proficiency in configuring and deploying applications on Linux-based systems 5+ years of experience Spark - especially Pyspark for transforming large non-structured text data, creating highly optimized pipelines Experience with RDBMS, ETL techniques and frameworks (Sqoop, Flume) and big data querying tools (Pig, Hive) Stickler of world class best practices, uncompromising on the quality of engineering, understand standards and reference architectures and deep in Unix philosophy with appreciation of big data design patterns, orthogonal code design and functional computation models |