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MIS/ Data Analyst perform various functions, which primarily involve helping an organization meet its strategic goals through providing direction and guidance concerning information processes, security of data, and software application management.
However, the major tasks, duties, and responsibilities that commonly define the MIS/ Data Analyst job description are listed below:
- Carry out analysis and assessment on the organization/clients existing software and database management systems, and information technology need to identify the shortcomings and proffer solutions to meet clients’ needs within time and cost constraints.
- Responsible for the design and development of software and computer systems to meet the project needs within time and cost constraints.
- Responsible for developing e-Commerce, database driven web sites, or other technology-driven solutions to improve the organization/client's processes using Windows and Java technologies.
- Resolve end user hardware and software issues in a timely and professional manner.
- Responsible for the development and maintenance of documentation and metrics to improve operational efficiency.
- Carry out research and evaluate new technologies that can improve existing services.
- Work together with other members of the MIS team to cross-train and provide backup to other team members.
- Appraise and write software and database business systems analysis report on a framework, as well as update user knowledge for developed systems.
- Perform system monitoring and analysis, and performance tuning to track system performance.
- Responsible for troubleshooting system hardware, software, networks, and operating systems
- Develop system access criteria, monitor, and control all system access; and implement security controls to secure computer system and ensure data integrity.
- Provide IT support to the user community by interfacing with them.
Requirements:
- Education: To work as an MIS analyst, applicants require a minimum of bachelor's degree in MIS, ISM, or Operations Research; Business Computer Science, Management Information Systems, or a field related to the employer’s industry.
- Experience: Minimum 4 years of working experience in the same domain with advanced knowledge of MS Excel & tools like PowerBI, Tableau.
- Knowledge: It is important that applicants have practical experience in business analytics or intelligence. They also require a working knowledge of a variety of data analysis techniques, including clustering, factor analysis, and logistic regression; data/text mining, decision trees, etc.
- The MIS/ Data Analyst job requires that they undertake research to solve business problems, so it is vital that applicants can manipulate and extract insights from large and complex datasets and are proficient with SQL and tableau. It is also vital that they are comfortable with financial analysis to successfully perform some aspects of their job.
- Computer skills: To succeed as an MIS/ Data Analyst, applicants must be advanced with Microsoft Tools, including Excel and possess advanced computer skills for data extraction, manipulation, and data analysis. Having practical experience with Power BI, Python etc.
- Problem-solving skills: To perform their job successfully, the MIS/ Data Analyst must be curious individuals with analytical abilities to examine the current processes of an organization and come up with creative ways of solving them.
- Communication skills: Their job requires that they communicate gaps to management or clients and train staff on how to use new or revised systems implemented. So, it is crucial that applicants are excellent communicators who can clarify expectations and drive alignment.
- Collaborative skills: It is also important that applicants can work together with different teams across the organization with a commitment to deadlines and deliverables.
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About SmartWinnr
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- A Natural Language Processing (NLP) expert with strong computer science fundamentals and experience in working with deep learning frameworks. You will be working at the cutting edge of NLP and Machine Learning.
Roles and Responsibilities
- Work as part of a distributed team to research, build and deploy Machine Learning models for NLP.
- Mentor and coach other team members
- Evaluate the performance of NLP models and ideate on how they can be improved
- Support internal and external NLP-facing APIs
- Keep up to date on current research around NLP, Machine Learning and Deep Learning
Mandatory Requirements
- Any graduation with at least 2 years of demonstrated experience as a Data Scientist.
Behavioral Skills
Strong analytical and problem-solving capabilities.
- Proven ability to multi-task and deliver results within tight time frames
- Must have strong verbal and written communication skills
- Strong listening skills and eagerness to learn
- Strong attention to detail and the ability to work efficiently in a team as well as individually
Technical Skills
Hands-on experience with
- NLP
- Deep Learning
- Machine Learning
- Python
- Bert
Preferred Requirements
- Experience in Computer Vision is preferred
Graas uses predictive AI to turbo-charge growth for eCommerce businesses. We are “Growth-as-a-Service”. Graas is a technology solution provider using predictive AI to turbo-charge growth for eCommerce businesses. Graas integrates traditional data silos and applies a machine-learning AI engine, acting as an in-house data scientist to predict trends and give real-time insights and actionable recommendations for brands. The platform can also turn insights into action by seamlessly executing these recommendations across marketplace store fronts, brand.coms, social and conversational commerce, performance marketing, inventory management, warehousing, and last mile logistics - all of which impacts a brand’s bottom line, driving profitable growth.
Roles & Responsibilities:
Work on implementation of real-time and batch data pipelines for disparate data sources.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS technologies.
- Build and maintain an analytics layer that utilizes the underlying data to generate dashboards and provide actionable insights.
- Identify improvement areas in the current data system and implement optimizations.
- Work on specific areas of data governance including metadata management and data quality management.
- Participate in discussions with Product Management and Business stakeholders to understand functional requirements and interact with other cross-functional teams as needed to develop, test, and release features.
- Develop Proof-of-Concepts to validate new technology solutions or advancements.
- Work in an Agile Scrum team and help with planning, scoping and creation of technical solutions for the new product capabilities, through to continuous delivery to production.
- Work on building intelligent systems using various AI/ML algorithms.
Desired Experience/Skill:
- Must have worked on Analytics Applications involving Data Lakes, Data Warehouses and Reporting Implementations.
- Experience with private and public cloud architectures with pros/cons.
- Ability to write robust code in Python and SQL for data processing. Experience in libraries such as Pandas is a must; knowledge of one of the frameworks such as Django or Flask is a plus.
- Experience in implementing data processing pipelines using AWS services: Kinesis, Lambda, Redshift/Snowflake, RDS.
- Knowledge of Kafka, Redis is preferred
- Experience on design and implementation of real-time and batch pipelines. Knowledge of Airflow is preferred.
- Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
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Job brief
We are looking for a Data Scientist to analyze large amounts of raw information to find patterns that will help improve our company. We will rely on you to build data products to extract valuable business insights.
In this role, you should be highly analytical with a knack for analysis, math and statistics. Critical thinking and problem-solving skills are essential for interpreting data. We also want to see a passion for machine-learning and research.
Your goal will be to help our company analyze trends to make better decisions.
Requirements
1. 2 to 4 years of relevant industry experience
2. Experience in Linear algebra, statistics & Probability skills, such as distributions, Deep Learning, Machine Learning
3. Strong mathematical and statistics background is a must
4. Experience in machine learning frameworks such as Tensorflow, Caffe, PyTorch, or MxNet
5. Strong industry experience in using design patterns, algorithms and data structures
6. Industry experience in using feature engineering, model performance tuning, and optimizing machine learning models
7. Hands on development experience in Python and packages such as NumPy, Sci-Kit Learn and Matplotlib
8. Experience in model building, hyper
Experience in developing lambda functions with AWS Lambda
Expertise with Spark/PySpark – Candidate should be hands on with PySpark code and should be able to do transformations with Spark
Should be able to code in Python and Scala.
Snowflake experience will be a plus
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Data Warehouse and Analytics solutions that aggregate data across diverse sources and data types
including text, video and audio through to live stream and IoT in an agile project delivery
environment with a focus on DataOps and Data Observability. You will work with Azure SQL
Databases, Synapse Analytics, Azure Data Factory, Azure Datalake Gen2, Azure Databricks, Azure
Machine Learning, Azure Service Bus, Azure Serverless (LogicApps, FunctionApps), Azure Data
Catalogue and Purview among other tools, gaining opportunities to learn some of the most
advanced and innovative techniques in the cloud data space.
You will be building Power BI based analytics solutions to provide actionable insights into customer
data, and to measure operational efficiencies and other key business performance metrics.
You will be involved in the development, build, deployment, and testing of customer solutions, with
responsibility for the design, implementation and documentation of the technical aspects, including
integration to ensure the solution meets customer requirements. You will be working closely with
fellow architects, engineers, analysts, and team leads and project managers to plan, build and roll
out data driven solutions
Expertise:
Proven expertise in developing data solutions with Azure SQL Server and Azure SQL Data Warehouse (now
Synapse Analytics)
Demonstrated expertise of data modelling and data warehouse methodologies and best practices.
Ability to write efficient data pipelines for ETL using Azure Data Factory or equivalent tools.
Integration of data feeds utilising both structured (ex XML/JSON) and flat schemas (ex CSV,TXT,XLSX)
across a wide range of electronic delivery mechanisms (API/SFTP/etc )
Azure DevOps knowledge essential for CI/CD of data ingestion pipelines and integrations.
Experience with object-oriented/object function scripting languages such as Python, Java, JavaScript, C#,
Scala, etc is required.
Expertise in creating technical and Architecture documentation (ex: HLD/LLD) is a must.
Proven ability to rapidly analyse and design solution architecture in client proposals is an added advantage.
Expertise with big data tools: Hadoop, Spark, Kafka, NoSQL databases, stream-processing systems is a plus.
Essential Experience:
5 or more years of hands-on experience in a data architect role with the development of ingestion,
integration, data auditing, reporting, and testing with Azure SQL tech stack.
full data and analytics project lifecycle experience (including costing and cost management of data
solutions) in Azure PaaS environment is essential.
Microsoft Azure and Data Certifications, at least fundamentals, are a must.
Experience using agile development methodologies, version control systems and repositories is a must.
A good, applied understanding of the end-to-end data process development life cycle.
A good working knowledge of data warehouse methodology using Azure SQL.
A good working knowledge of the Azure platform, it’s components, and the ability to leverage it’s
resources to implement solutions is a must.
Experience working in the Public sector or in an organisation servicing Public sector is a must,
Ability to work to demanding deadlines, keep momentum and deal with conflicting priorities in an
environment undergoing a programme of transformational change.
The ability to contribute and adhere to standards, have excellent attention to detail and be strongly driven
by quality.
Desirables:
Experience with AWS or google cloud platforms will be an added advantage.
Experience with Azure ML services will be an added advantage Personal Attributes
Articulated and clear in communications to mixed audiences- in writing, through presentations and one-toone.
Ability to present highly technical concepts and ideas in a business-friendly language.
Ability to effectively prioritise and execute tasks in a high-pressure environment.
Calm and adaptable in the face of ambiguity and in a fast-paced, quick-changing environment
Extensive experience working in a team-oriented, collaborative environment as well as working
independently.
Comfortable with multi project multi-tasking consulting Data Architect lifestyle
Excellent interpersonal skills with teams and building trust with clients
Ability to support and work with cross-functional teams in a dynamic environment.
A passion for achieving business transformation; the ability to energise and excite those you work with
Initiative; the ability to work flexibly in a team, working comfortably without direct supervision.
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Requirements:
- Overall 3 to 5 years of experience in designing and implementing complex large scale Software.
- Good in Python is must.
- Experience in Apache Spark, Scala, Java and Delta Lake
- Experience in designing and implementing templated ETL/ELT data pipelines
- Expert level experience in Data Pipeline Orchestrationusing Apache Airflow for large scale production deployment
- Experience in visualizing data from various tasks in the data pipeline using Apache Zeppelin/Plotly or any other visualization library.
- Log management and log monitoring using ELK/Grafana
- Git Hub Integration
Technology Stack: Apache Spark, Apache Airflow, Python, AWS, EC2, S3, Kubernetes, ELK, Grafana , Apache Arrow, Java
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About the Company:
This opportunity is for an AI Drone Technology startup funded by the Indian Army. It is working to develop cutting-edge products to help the Indian Army gain an edge in New Age Enemy Warfare.
They are working on using drones to neutralize terrorists hidden in deep forests. Get a chance to contribute to secure our borders against the enemy.
Responsibilities:
- Extensive knowledge in machine learning and deep learning techniques
- Solid background in image processing/computer vision
- Experience in building datasets for computer vision tasks
- Experience working with and creating data structures/architectures
- Proficiency in at least one major machine learning framework such as Tensorflow, Pytorch
- Experience visualizing data to stakeholders
- Ability to analyze and debug complex algorithms
- Highly skilled in Python scripting language
- Creativity and curiosity for solving highly complex problems
- Excellent communication and collaboration skills
Educational Qualification:
MS in Engineering, Applied Mathematics, Data Science, Computer Science or equivalent field, with 3 years industry experience, a PhD degree or equivalent industry experience.
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We are looking for an outstanding ML Architect (Deployments) with expertise in deploying Machine Learning solutions/models into production and scaling them to serve millions of customers. A candidate with an adaptable and productive working style which fits in a fast-moving environment.
Skills:
- 5+ years deploying Machine Learning pipelines in large enterprise production systems.
- Experience developing end to end ML solutions from business hypothesis to deployment / understanding the entirety of the ML development life cycle.
- Expert in modern software development practices; solid experience using source control management (CI/CD).
- Proficient in designing relevant architecture / microservices to fulfil application integration, model monitoring, training / re-training, model management, model deployment, model experimentation/development, alert mechanisms.
- Experience with public cloud platforms (Azure, AWS, GCP).
- Serverless services like lambda, azure functions, and/or cloud functions.
- Orchestration services like data factory, data pipeline, and/or data flow.
- Data science workbench/managed services like azure machine learning, sagemaker, and/or AI platform.
- Data warehouse services like snowflake, redshift, bigquery, azure sql dw, AWS Redshift.
- Distributed computing services like Pyspark, EMR, Databricks.
- Data storage services like cloud storage, S3, blob, S3 Glacier.
- Data visualization tools like Power BI, Tableau, Quicksight, and/or Qlik.
- Proven experience serving up predictive algorithms and analytics through batch and real-time APIs.
- Solid working experience with software engineers, data scientists, product owners, business analysts, project managers, and business stakeholders to design the holistic solution.
- Strong technical acumen around automated testing.
- Extensive background in statistical analysis and modeling (distributions, hypothesis testing, probability theory, etc.)
- Strong hands-on experience with statistical packages and ML libraries (e.g., Python scikit learn, Spark MLlib, etc.)
- Experience in effective data exploration and visualization (e.g., Excel, Power BI, Tableau, Qlik, etc.)
- Experience in developing and debugging in one or more of the languages Java, Python.
- Ability to work in cross functional teams.
- Apply Machine Learning techniques in production including, but not limited to, neuralnets, regression, decision trees, random forests, ensembles, SVM, Bayesian models, K-Means, etc.
Roles and Responsibilities:
Deploying ML models into production, and scaling them to serve millions of customers.
Technical solutioning skills with deep understanding of technical API integrations, AI / Data Science, BigData and public cloud architectures / deployments in a SaaS environment.
Strong stakeholder relationship management skills - able to influence and manage the expectations of senior executives.
Strong networking skills with the ability to build and maintain strong relationships with both business, operations and technology teams internally and externally.
Provide software design and programming support to projects.
Qualifications & Experience:
Engineering and post graduate candidates, preferably in Computer Science, from premier institutions with proven work experience as a Machine Learning Architect (Deployments) or a similar role for 5-7 years.
Hi All,
We are hiring Data Engineer for one of our client for Bangalore & Chennai Location.
Strong Knowledge of SCCM, App V, and Intune infrastructure.
Powershell/VBScript/Python,
Windows Installer
Knowledge of Windows 10 registry
Application Repackaging
Application Sequencing with App-v
Deploying and troubleshooting applications, packages, and Task Sequences.
Security patch deployment and remediation
Windows operating system patching and defender updates
Thanks,
Mohan.G
• Responsible for developing and maintaining applications with PySpark
Must Have Skills:
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