Fusemachines Hiring Drive 2024: Fusemachines Hiring Drive 2024 for its Data Analyst. Fusemachines Drive organizes off-campus drives for candidates in any batch. To apply, candidates should have a bachelor’s or master’s degree in a quantitative field such as statistics, mathematics, or computer science. if you are interested, please apply as soon as possible.
Company Name: Fusemachines
Role: Data Analyst
Location: Pune
Experience: 1 – 3 years
Qualification: Bachelors or masters degree in a quantitative field such as statistics, mathematics, or computer science
Batch: Any
Job Type: Full Time
Salary: Best in Industry
Fusemachines Hiring Drive 2024
Job Description:
- This is a full-time position, working in the Healthcare Insurance Industry, responsible for building the Business Intelligence (gathering, interpreting, analyzing and visualizing large and complex datasets to provide insights and support data-driven decision-making within the organization), with knowledge of Machine-readable Files (MRF) to support a solution for Pricing Information Published under the Transparency in Coverage (TiC) Final Rule
- We are seeking a talented and experienced Data Analyst with expertise in PowerBI, Azure, Snowflake and Healthcare Insurance to join our team.
- The ideal candidate will work in an Agile environment, contributing to the architecture, design, implementation and testing of Business Intelligence in the Healthcare Insurance Industry.
- This role involves hands-on coding and collaboration with multi-disciplined teams to achieve project objectives.
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Requirements:
- Bachelors or masters degree in a quantitative field such as statistics, mathematics, or computer science.
- At least 3 years of experience in data analytics, with a focus on business intelligence and data visualization, delivering large scale projects and products for Data and Analytics in the Healthcare Insurance industry.
- At least 3 years of professional experience developing and implementing enterprise-scale reports and highly visual, intuitive dashboards.
- tech stack mandatory (Azure, Power BI, Power Automate, Report Builder, SQL Server, DevOps, GIT) and Snowflake.
- Preferred Certifications:Microsoft Certified: Azure Fundamentals Microsoft Certified: Power BI Data Analyst SnowPro Advanced Data Analyst
- Highly skilled in data quality, triage analysis and obtaining business insights, desirable specialization in Healthcare Insurance.
- Highly skilled in Data Visualization, specifically in PowerBI.
- Strong SQL skills and experience working with complex data sets, Enterprise Data Warehouse and writing advanced SQL queries.
- Proficient with Relational Databases (Oracle, SQL Server, MySQL, Postgres, or similar) and NonSQL Databases (Cassandra, MongoDB, Neo4j, etc).
- Knowledge in Data Warehousing, data lake and data lake house, solutions in Azure (blob storage and Azure Data Lake Storage Gen2), Databricks and Snowflake.
- Good programming Skills in one or more languages such as Python.
- Expertise in data cleansing, transformation, and validation.
- Good understanding of Data Modeling and Database Design Principles.
- Being able to design and implement efficient database schemas that meet the requirements of the data architecture to support data solutions, for delivering a modern analytics solution (descriptive, diagnostic, predictive, prescriptive) within Snowflake.
- Strong analytical and problem-solving skills with the ability to translate complex data into actionable insights.
- Attention to Detail: Being meticulous and paying attention to detail is critical in data analysis.
- Small errors or inaccuracies can lead to misleading results, so data analysts should have a keen eye for detail and double-check their work.
- Strong analytical skills to identify and address technical issues, performance bottlenecks, and system failures.
- Strong understanding of the software development lifecycle (SDLC), especially Agile methodologies.
- Strong knowledge of SDLC tools and technologies Azure DevOps and GitHub, including project management software (Jira, Azure Boards or similar), source code management (GitHub, Azure Repos or similar), CI/CD system (GitHub actions, Azure Pipelines, Jenkins or similar) and binary repository manager (Azure Artifacts or similar).
- Good understanding of Data Quality and Governance, including implementation of data quality and integrity checks and monitoring processes to ensure that data is accurate, complete, and consistent.
- Effective communication skills to collaborate with cross-functional teams, including business users, data architects, DevOps/DataOps/MLOps engineers, data engineers, data scientists, developers, and operations teams.
- Essential to convey complex technical concepts and insights to non-technical stakeholders effectively.
- Self-motivated with the ability to work well in an agile team.
- Possesses strong leadership skills with a willingness to lead, create Ideas, and be assertive.
- A willingness to stay updated with the latest services, Data Analytics trends, and best practices in the field.
Responsibility:
- Data Collection and modeling: Gathering and exploring data from various sources such as EDWH, databases, spreadsheets, APIs, and other relevant sources to support business requirements, and creating the corresponding analytical layer.
- Data Cleaning and Preprocessing: Reviewing and organizing data to ensure accuracy, consistency, and completeness.
- This may involve handling missing values, removing outliers, and transforming data into a suitable format for analysis.
- Data Analysis: Proactively applying statistical techniques and analytical methods to examine data, test hypothesis and identify patterns, trends, relationships and insights that inform business decisions, identifying critical metrics and KPIs, to answer complex key questions from stakeholders with an eye for what drives business performance, to draw meaningful conclusions from data and make data-driven recommendations.
- This will involve using tools like SQL, PowerBI, Python or specialized data analysis software.
- Data Visualization : Design, build and maintain rich interactive visual representations of data through charts, graphs, and dashboards from multiple data sources to communicate insights effectively to stakeholders.
- Using tools like PowerBI and Python libraries (eg, Matplotlib, Seaborn).
- Reporting: Summarizing and presenting findings from data analysis in a clear and concise manner.
- This includes creating reports, slide decks, or presentations to communicate insights and recommendations to non-technical stakeholders.
- Data Governance including Quality Assurance: Ensuring the accuracy, consistency, and integrity of data by performing quality checks and validation procedures.
- This involves identifying and resolving data discrepancies or errors in: MRF, and pricing information.
- Data Mining: Identifying patterns, trends, and correlations in large datasets to extract meaningful information and support business objectives.
- This may involve using techniques like clustering, classification, regression, or association analysis.
- Identifying and implementing best practices for business intelligence, data visualization, reporting and analysis, building self-service capabilities for business users.
- Business Intelligence Reporting Capabilities, in real-time and supporting filtering and drill-down capabilities, for MRF data, supporting client-facing teams regarding failures at each step in the data flow.
- Including auditing capabilities so that stakeholders can easily determine why a specific negotiated rate does not appear in the pricing tool.
- Work in an agile team focused on Healthcare Insurance projects.
- Collaborating with Teams: Working closely with cross-functional teams, such as business analysts, data engineers, data scientists and decision-makers, to understand their requirements, provide analytical support, identify key metrics and contribute to data-driven initiatives to solve business challenges.
- Maintain documentation of their work, including code.
- Communicating to the different stakeholders the progress of the tasks assigned and discussing any issues or blockers.
- Continuous Learning: Staying updated with industry trends, new analytical techniques, and tools to enhance data analysis capabilities and improve efficiency.
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