Highlights
Key service lines include Cybersecurity, Digital Core, Finance, Industry and Enterprise, Song, Supply Chain and Engineering, and Talent, with advanced capabilities in AI and Data, Industry and Process, and Technology. Run structured stakeholder interviews with data owners, OPMs, manufacturing ops, quality, and engineering to capture how each dataset is actually produced and used, not just how it's documented.
Numbers & Facts
| Location | Sunnyvale, CA |
| Industry | Computer/IT Services |
| Salary | $80–$85 |
| Company Size | 100 to 499 employees |
| Year Founded | 2009 |
| Website | http://www.tekwissen.com/ |
Description
Overview:
Accenture helps the world's leading enterprises reinvent by building their digital core and unleashing the power of AI to create value at speed for organizations across industries. With ~786,000 employees worldwide, it serves approximately 9,000 clients in 120+ countries across 40+ industries transform their operations through technology and human ingenuity. Key service lines include Cybersecurity, Digital Core, Finance, Industry and Enterprise, Song, Supply Chain and Engineering, and Talent, with advanced capabilities in AI and Data, Industry and Process, and Technology.
Job Title: Data Scientist, Dataset Assessment Lead
Location: Sunnyvale, CA
Duration: 3 Months
Job Type: Temporary Assignment
Work Type: Hybrid (50%)
About the project
Reality Labs Supply Chain is standing up an NPI data foundation to support AI-enabled supply chain workstreams (product change management / PRQ qualification, capacity and Capex planning, in-market quality and incident management, and end-of-manufacturing planning). Before we build, we need an honest inventory and quality baseline of the datasets these workstreams depend on. This role owns that assessment.
Accenture helps the world's leading enterprises reinvent by building their digital core and unleashing the power of AI to create value at speed for organizations across industries. With ~786,000 employees worldwide, it serves approximately 9,000 clients in 120+ countries across 40+ industries transform their operations through technology and human ingenuity. Key service lines include Cybersecurity, Digital Core, Finance, Industry and Enterprise, Song, Supply Chain and Engineering, and Talent, with advanced capabilities in AI and Data, Industry and Process, and Technology.
Job Title: Data Scientist, Dataset Assessment Lead
Location: Sunnyvale, CA
Duration: 3 Months
Job Type: Temporary Assignment
Work Type: Hybrid (50%)
About the project
Reality Labs Supply Chain is standing up an NPI data foundation to support AI-enabled supply chain workstreams (product change management / PRQ qualification, capacity and Capex planning, in-market quality and incident management, and end-of-manufacturing planning). Before we build, we need an honest inventory and quality baseline of the datasets these workstreams depend on. This role owns that assessment.
What you'll do
Build the authoritative inventory of datasets supporting NPI and supply chain processes - source systems, owners, refresh cadence, lineage, downstream consumers.
Run structured stakeholder interviews with data owners, OPMs, manufacturing ops, quality, and engineering to capture how each dataset is actually produced and used, not just how it's documented.
Score every dataset against the 7-dimension data quality framework [7 dimensions: ], with evidence and a defensible rationale per score.
Define and document the scoring rubric, interview guide, and intake template so the work is repeatable after the engagement ends.
Deliver a prioritized remediation backlog: which datasets are fit for AI/automation use today, which need work, and what that work is.
Hand off a clear findings readout to Supply Chain leadership and to the incoming Data Engineer.
Build the authoritative inventory of datasets supporting NPI and supply chain processes - source systems, owners, refresh cadence, lineage, downstream consumers.
Run structured stakeholder interviews with data owners, OPMs, manufacturing ops, quality, and engineering to capture how each dataset is actually produced and used, not just how it's documented.
Score every dataset against the 7-dimension data quality framework [7 dimensions: ], with evidence and a defensible rationale per score.
Define and document the scoring rubric, interview guide, and intake template so the work is repeatable after the engagement ends.
Deliver a prioritized remediation backlog: which datasets are fit for AI/automation use today, which need work, and what that work is.
Hand off a clear findings readout to Supply Chain leadership and to the incoming Data Engineer.
Minimum qualifications
5+ years in data science, data quality, or data governance roles.
Demonstrated experience building a dataset inventory or running a data quality assessment at enterprise scale.
Working knowledge of supply chain, manufacturing, or NPI processes - enough to interrogate a forecast, capacity, yield, or inventory dataset credibly.
Strong SQL plus Python (pandas) for profiling and statistical quality checks.
Excellent stakeholder interviewing and written synthesis skills; comfortable driving conversations with senior XFN partners.
Able to deliver a scoped assessment in an 8-week window with minimal ramp.
5+ years in data science, data quality, or data governance roles.
Demonstrated experience building a dataset inventory or running a data quality assessment at enterprise scale.
Working knowledge of supply chain, manufacturing, or NPI processes - enough to interrogate a forecast, capacity, yield, or inventory dataset credibly.
Strong SQL plus Python (pandas) for profiling and statistical quality checks.
Excellent stakeholder interviewing and written synthesis skills; comfortable driving conversations with senior XFN partners.
Able to deliver a scoped assessment in an 8-week window with minimal ramp.
Preferred qualifications
Familiarity with Meta's data stack (Hive/Presto, Databricks, Scuba, Unidash) and with PLM/MES data (Teamcenter, factory MES).
Experience with data catalog / metadata management tooling and formal DQ frameworks (DAMADMBOK or similar).
Hardware NPI or consumer electronics supply chain background.
TekWissen Group is an equal opportunity employer supporting workforce diversity.
Familiarity with Meta's data stack (Hive/Presto, Databricks, Scuba, Unidash) and with PLM/MES data (Teamcenter, factory MES).
Experience with data catalog / metadata management tooling and formal DQ frameworks (DAMADMBOK or similar).
Hardware NPI or consumer electronics supply chain background.
TekWissen Group is an equal opportunity employer supporting workforce diversity.
About Company
WE THE TEKWISSEN PEOPLE
TekWissen offers you a broader portfolio of services, industry-leading solutions, and the meaningful innovations that give you greater flexibility and speed to respond to market dynamics, reduced costs and risk to improve enterprise performance, and increased productivity to enable growth.
To keep pace with global market demands, TekWissen keeps its finger on the pulse of change. Our organized approach to guiding a project from its inception to closure. Managing projects is becoming more and more important as we enter the digital era. To cope with the pace that this transition demands, a method is required to manage projects so they can yield quality work, while incorporating efficient use of time and resources.
Project involves identifying which quality standards are relevant to the project and determining how to satisfy them.
It is important to perform quality planning during the Planning Process and should be done alongside the other project planning processes because changes in the quality will likely require changes in the other planning processes, or the desired product quality may require a detailed risk analysis of an identified problem. It is important to remember that quality should be planned, designed, then built in, not added on after the fact.
Capabilities and accomplishments in one TekWissen business enhance the opportunity for success in the others. Put simply, TekWissen's unique combination of attributes promotes success.Similar Job Searches
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