Data Scientist 1
Capgemini
Employer Active
Posted 1 hrs ago
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Experience
1 - 7 Years
Job Location
Education
Bachelor of Science(Statistics)
Nationality
Any Nationality
Gender
Not Mentioned
Vacancy
1 Vacancy
Job Description
Roles & Responsibilities
We are seeking an experienced and highly skilled Data Scientist to join our dynamic team.
- Develop and implement machine learning models to solve complex business problems.
- Analyze large datasets using statistical and machine learning techniques.
- Work with structured and unstructured data, and design advanced algorithms for data mining, predictive modeling, NLP, and time series analysis.
- Collaborate with cross-functional teams to integrate data science solutions into cloud-based environments (AWS, Azure, GCP, Databricks, Snowflake).
- Build data pipelines and manage big data frameworks for efficient data processing.
- Use data visualization tools like Tableau, Microsoft Power BI, Matplotlib, and Seaborn to present complex results in a clear and concise manner.
- Programming Languages: Expertise in Python, R. Data Science Platforms:
- Experience with Databricks, Dataiku, AzureML, SageMaker.
- Data Analysis Tools: Proficiency in Pandas, NumPy.
- Cloud Platforms: Strong understanding of Snowflake, Azure, AWS, GCP, Databricks.
- Strong SQL skills for data manipulation and querying.
- Machine Learning & AI: Experience in building and deploying machine learning models, including Natural Language Processing (NLP).
- Big Data Technologies: Familiarity with big data technologies and processing.
- Time Series Analysis: Strong knowledge of time series forecasting and analysis.
- Data Visualization Tools: Hands-on experience with Matplotlib, Seaborn, Tableau, Microsoft Power BI.
- Bachelor's degree in data science, Analytics, Business, or related field. Advanced degrees or certifications are a plus.
Desired Candidate Profile
- Develop and implement machine learning models to solve complex business problems.
- Analyze large datasets using statistical and machine learning techniques.
- Work with structured and unstructured data, and design advanced algorithms for data mining, predictive modeling, NLP, and time series analysis.
- Collaborate with cross-functional teams to integrate data science solutions into cloud-based environments (AWS, Azure, GCP, Databricks, Snowflake).
- Build data pipelines and manage big data frameworks for efficient data processing.
- Use data visualization tools like Tableau, Microsoft Power BI, Matplotlib, and Seaborn to present complex results in a clear and concise manner.
- Programming Languages: Expertise in Python, R. Data Science Platforms:
- Experience with Databricks, Dataiku, AzureML, SageMaker.
- Data Analysis Tools: Proficiency in Pandas, NumPy.
- Cloud Platforms: Strong understanding of Snowflake, Azure, AWS, GCP, Databricks.
- Strong SQL skills for data manipulation and querying.
- Machine Learning & AI: Experience in building and deploying machine learning models, including Natural Language Processing (NLP).
- Big Data Technologies: Familiarity with big data technologies and processing.
- Time Series Analysis: Strong knowledge of time series forecasting and analysis.
- Data Visualization Tools: Hands-on experience with Matplotlib, Seaborn, Tableau, Microsoft Power BI.
- Bachelor's degree in data science, Analytics, Business, or related field. Advanced degrees or certifications are a plus.
Company Industry
Department / Functional Area
Keywords
- Data Scientist 1
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Capgemini
Capgemini Global Insights & Data business line is a market leader in the data, platform, and analytics across all regions and across many sectors (including financial services, consumer products, manufacturing & life sciences). Our offerings include end-2-end data integration to cloud platform, complete suite of AI engineering capabilities and a category of sector-based advanced analytics / AI solutions. The places that you work from day to day will vary according to your role, your needs, and those of the business; it will be a blend of Company offices, client sites, and your home; noting that you will be unable to work at home 100% of the time.