Data Scientist - Qualified Pipeline at Data Meaning

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Full Time

Start Date

Immediate

Expiry Date

06 Apr, 26

Salary

0.0

Posted On

06 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, R, Power BI, Tableau, Data Analysis, Data Mining, Statistical Modeling, Big Data Infrastructure, Machine Learning, Predictive Analytics, Data Visualization, SQL, NoSQL, Data Integrity, Data Processing, Data Exploration

Industry

IT Services and IT Consulting

Description

Data Scientist - Qualified Pipeline Location: Latam/US Position Pay: Salary depends on experience POSITION SUMMARY: Data Meaning is a front-runner in Business Intelligence and Data Analytics consulting, renowned for our high-quality consulting services throughout the US and LATAM. Our expertise lies in delivering tailored solutions in Business Intelligence, Data Warehousing, and Project Management. As a Tableau Preferred Partner and the distinguished Alteryx 2020 Global Partner of the Year, we are dedicated to bringing premier services to our diverse clientele. Our strength lies in our global team of 95+ consultants, all working remotely, embodying a collaborative, inclusive, and innovation-driven work culture. We are proactively building a pipeline for qualified Data Scientists to joinour team in the near future. The IDEAL candidate will have experience with either Python or R, Power BI or Tableau, and excellent understanding of Data Analysis, Data Mining, Statistic Modeling and Big Data Infrastructure. RESPONSIBILITIES: Follow consistent practices to ensure data integrity and deal with imperfections in data. Effectively process structured and semi-structured/unstructured data; proficiently integrate varied datasets Work cross-functionally with development and engineering teams Analyze large amounts of information to discover trends and patterns, draw conclusions and gain actionable insights. Build predictive models; propose solutions and strategies for business problems Apply the appropriate machine learning algorithm to data problems (e.g. supervised vs. unsupervised machine learning, clustering, ensemble methods, etc.), validate model results. Work closely with architecture and engineering team to deploy models; communicate results in a clear and non-technical manner. Interact with senior management and effectively communicate technical information to numerous audiences (e.g. senior management, client community, peers and junior associates) Address challenges to analytic processes and influence others to change their approach and work collectively to implement new tools/techniques Gain support from related stakeholders for analytic solutions and champion data driven business decisions. Apply appropriate statistical techniques to data exploration and model development/assessment Communicate complex statistical concepts to non-technical audiences; maintain skills through continuing education. Create meaningful data visualizations to communicate results and highlight business impact Effectively collaborate with others to deliver efficient and high-quality code (Python or R) Implement solid validation process to ensure consistency and minimize errors in code Champion more efficient ways to produce code, maintain skills through continuing education; stay up to date on new technologies/ tools Seamlessly transition between languages to apply the right approach to the problem. Understanding of Big Data infrastructure – Hadoop, MapReduce and Spark REQUIREMENTS: Bachelor’s degree in Computer Science, Math or Statistics is preferred At least 4 years of professional experience in a data-related role Good communication skills (English proficiency) Knowledge of Statistics and Statistical Modeling Understanding of Machine Learning and Predictive Analytics Data analysis programming languages – Python and R (at least one) Database languages – SQL and NoSQL (desirable) Data Visualization – Power BI or Tableau

Responsibilities

The Data Scientist will ensure data integrity, process various data types, and analyze large datasets to discover trends and insights. They will build predictive models, apply machine learning algorithms, and communicate results effectively to diverse audiences.