Azure Lead Data Scientist
for sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Use - for any list of duties/requirements/benefits, and
for prose. Do NOT repeat job title as heading. Do NOT put Job ID / requisition numbers as headings — fold minor metadata into short
or drop it. Preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. IMPORTANT: include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text (e.g. "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. Prefer a few substantive sections over many thin ones.
We need to parse the posting.
Key content:
- Intro paragraph about MCA Connect (company description). Could be "About the role"? Actually "About the role" is a heading for description of role. The intro about company could be part of "About the role" or maybe separate? The instruction: Use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have sections: About the role, Responsibilities, Qualifications, Benefits, Pay. Possibly Skills (maybe part of Qualifications). We need to decide.
We need to preserve all substantive information.
Let's extract:
Company description: "Through passion and deep industry expertise, MCA Connect helps manufacturers succeed by unlocking innovation with actionable business insights. Our strategic solutions, innovation, and industry intelligence help manufacturers gain visibility, improve profitability, and achieve a competitive edge. Established in 2002, MCA Connect has grown into one of the largest US-based solution partners in Microsoft Business Applications and Azure Data & AI / Digital & App Innovation. Our Microsoft Specialties include Finance and Supply Chain, Analytics on Azure, Data Warehouse Migration, and Power Platform. We’re also a fifteen-time Microsoft Partner of the Year and three-time Inc. Best Workplaces award winner."
That's about the company, not necessarily role. Could be placed under "About the role"? Usually "About the role" includes description of role and maybe company. We'll include it there.
Then Title: Senior Data Scientist (but we must not repeat job title as heading). So we can include title in a
maybe.
Location: Virtual, home based office with travel.
Description paragraph: "The Senior Data Scientist is responsible for being a partner to team members, a mentor, and directly enabling growth of the Manufacturing Intelligence team at MCA. The Sr. Data Scientist is expected to lead Data Science implementations, drive innovation, upskill and mentor junior teammates, and partner with leadership to drive continuous improvement. Within a project framework, the Sr. Data Scientist is responsible for building client relationships, effectively utilizing Agile project delivery methodologies, implementing scalable and efficient technical architectures to satisfy client requirements, and performing hands on implementations of Microsoft data solutions for MCA Connect’s current and future customers/projects."
That's part of responsibilities.
Then "Responsibilities" heading followed by bullet list (actually a paragraph with many sentences). We need to convert to
- for any list of duties/requirements/benefits. The responsibilities are many sentences; we can split each sentence into a list item.
Let's list responsibilities sentences:
- Leveraging advanced analytics techniques and tools such as Azure Machine Learning, Azure Open AI, Azure AI Search, Azure Synapse Analytics, Databricks, and Power BI
- Extract insights from large datasets and develop data-driven solutions.
- Lead end-to-end data science projects, collaborate with cross-functional teams, and stay updated with emerging Microsoft technologies.
- Strong problem-solving, programming, and communication skills. (This seems like a qualification but appears in responsibilities block)
- Building predictive models and other analytical solutions.
- Apply statistical and machine learning methods to analyze large, complex data sets.
- Collaborate with various parties to brainstorm creative uses for data.
- Develop complex algorithms and statistical predictive models and determine analytical approaches and modeling techniques to evaluate scenarios and potential future outcomes.
- Perform analyses of structured and unstructured data to solve multiple and complex business problems.
- Deliver actionable guidance and recommendations to stakeholders to help understand nuances of the models built and tactics for improving.
- Act as the subject matter expert on solutions produced by working closely with your peers to help Clients better understand their data.
- Actively participate in the execution and planning of data capture strategies to improve data automation, data quality, and analytics capabilities.
- Run ad-hoc data extracts for requests from diverse partners and automate those needs over time.
- Perform highly complex, technical, and creative predictive analytics projects.
- Manage and prioritize multiple moderate-to-highly complex projects including gathering business requirements, developing project goals and requirements, coordinating project timelines, and communicating project status and deliverables with Clients.
- Stay abreast of architectural/industry changes in related fields.
- Continuously improve Team processes to ensure information is of the highest quality, contributing to the overall effectiveness of the Team.
- Ensure best practices are followed and business objectives are achieved by focusing on process improvements.
- Meet with Clients to determine needs and/or identify problem areas.
- May be responsible for the development of Statement of Work and Proposals which outline professional services required to meet Client requirements.
- Must be open to ~10% travel on an as needed basis.
We need to ensure we don't duplicate content.
Now "Qualifications" heading: list of qualifications.
Qualifications bullet list:
- 7+ years of hands-on experience with data science, AI, and big data.
- Experience with data engineering is a plus.
- Degree in Information Technology or Business Administration or equivalent combination of education and experience
- Experience developing and owning production-level models using various data sets.
- Experience shipping models to production, and ensuring continuous availability via measurements, and tools like statistical process control.
- Ability to translate business needs into technical requirements through active partnerships, collaborating with partners, data scientists and data architects.
- Compelling storytelling through analytics and visualization, getting your point across, and keeping your audience engaged.
- Experience working in Python, R, Scala, Spark, and/or T-SQL.
- Extensive experience connecting to Data Platforms including data lakes, data warehouses, NoSQL databases, and APIs.
- Ability to set up data and experimental platforms.
- Strong critical thinking, active listening, and communication skills to infer business needs, grasp the underlying context, and translate loose direction on analytical projects into concrete solutions.
- Attention to detail and desire for end-to-end ownership of deliverables.
- Experience working on projects involving large volumes of data, preferably in the Manufacturing industry.
- Demonstrates the ability to communicate effectively with technical teams, business stakeholders, and other relevant parties
Note: Some of these overlap with responsibilities (e.g., strong problem-solving etc). We'll keep as given.
Then Pay: "$125,000 - $210,000 a year PLUS Uncapped Supplemental Compensation (Bonus) Plan paid out quarterly"
Benefits: "Why work for MCA Connect? Our Compensation Plan Offers One Of The Best Bonus Structures In The Industry. Along With This We Also Offer a Generous Benefit Package Work/Life Balance with Unlimited Paid Time Off (UPTO) 401k Plan with Company Matching Contribution Monthly Stipend for Home Office Expenses Subsidized Medical, Dental and Vision Coverage Health Savings and Flexible Spending Accounts Company Paid Life and Disability Insurance Training, Certification and Continuing Education Support MCA Connect offers limitless opportunities for personal and professional growth in a stimulating, challenging, and performance-oriented work culture where you can share your ideas and make impactful daily contributions. Our employees are highly motivated and talented individuals dedicated to developing, marketing, and selling products designed to deliver value for mid-market and enterprise-size manufacturing, distribution, and energy companies. We take the time to train our consultants so that they understand the industries we serve and can deliver best practices, proven methodologies, and ongoing industry expertise to our clients."
We need to extract benefits list: Unlimited Paid Time Off (UPTO), 401k Plan with Company Matching Contribution, Monthly Stipend for Home Office Expenses, Subsidized Medical, Dental and Vision Coverage, Health Savings and Flexible Spending Accounts, Company Paid Life and Disability Insurance, Training, Certification and Continuing Education Support. Also mention "Generous Benefit Package". We'll put as list.
Schedule: Not explicitly stated besides travel ~10% as needed. No explicit schedule (e.g., full-time, hours). So we should not create Schedule section unless posting actually states them. It does not state schedule, so omit.
Skills: Could be a section but we have qualifications covering skills. The instruction says prefer a few substantive sections over many thin ones. We could combine Skills into Qualifications or maybe have Skills separate if there is explicit skills list. There is not a separate skills heading; we could treat qualifications as skills. So we may not need a separate Skills section.
Thus sections: About the role (company description + role description), Responsibilities, Qualifications, Benefits, Pay.
We need to ensure we don't include boilerplate like EEO, AI tools, etc. Remove those.
Also remove duplicated lines: The posting had duplicated lines? Not many.
Now produce HTML fragment.
Structure:
About the role
Through passion and deep industry expertise, MCA Connect helps manufacturers succeed by unlocking innovation with actionable business insights. Our strategic solutions, innovation, and industry intelligence help manufacturers gain visibility, improve profitability, and achieve a competitive edge. Established in 2002, MCA Connect has grown into one of the largest US-based solution partners in Microsoft Business Applications and Azure Data & AI / Digital & App Innovation. Our Microsoft Specialties include Finance and Supply Chain, Analytics on Azure, Data Warehouse Migration, and Power Platform. We’re also a fifteen-time Microsoft Partner of the Year and three-time Inc. Best Workplaces award winner.
Title: Senior Data Scientist
Location: Virtual, home based office with travel
The Senior Data Scientist is responsible for being a partner to team members, a mentor, and directly enabling growth of the Manufacturing Intelligence team at MCA. The Sr. Data Scientist is expected to lead Data Science implementations, drive innovation, upskill and mentor junior teammates, and partner with leadership to drive continuous improvement. Within a project framework, the Sr. Data Scientist is responsible for building client relationships, effectively utilizing Agile project delivery methodologies, implementing scalable and efficient technical architectures to satisfy client requirements, and performing hands on implementations of Microsoft data solutions for MCA Connect’s current and future customers/projects.
Then Responsibilities:
Responsibilities
- Leveraging advanced analytics techniques and tools such as Azure Machine Learning, Azure Open AI, Azure AI Search, Azure Synapse Analytics, Databricks, and Power BI
- Extract insights from large datasets and develop data-driven solutions.
- Lead end-to-end data science projects, collaborate with cross-functional teams, and stay updated with emerging Microsoft technologies.
- Strong problem-solving, programming, and communication skills.
- Building predictive models and other analytical solutions.
- Apply statistical and machine learning methods to analyze large, complex data sets.
- Collaborate with various parties to brainstorm creative uses for data.
- Develop complex algorithms and statistical predictive models and determine analytical approaches and modeling techniques to evaluate scenarios and potential future outcomes.
- Perform analyses of structured and unstructured data to solve multiple and complex business problems.
- Deliver actionable guidance and recommendations to stakeholders to help understand nuances of the models built and tactics for improving.
- Act as the subject matter expert on solutions produced by working closely with your peers to help Clients better understand their data.
- Actively participate in the execution and planning of data capture strategies to improve data automation, data quality, and analytics capabilities.
for prose. Do NOT repeat job title as heading. Do NOT put Job ID / requisition numbers as headings — fold minor metadata into short
or drop it. Preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. IMPORTANT: include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text (e.g. "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. Prefer a few substantive sections over many thin ones. We need to parse the posting. Key content: - Intro paragraph about MCA Connect (company description). Could be "About the role"? Actually "About the role" is a heading for description of role. The intro about company could be part of "About the role" or maybe separate? The instruction: Use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have sections: About the role, Responsibilities, Qualifications, Benefits, Pay. Possibly Skills (maybe part of Qualifications). We need to decide. We need to preserve all substantive information. Let's extract: Company description: "Through passion and deep industry expertise, MCA Connect helps manufacturers succeed by unlocking innovation with actionable business insights. Our strategic solutions, innovation, and industry intelligence help manufacturers gain visibility, improve profitability, and achieve a competitive edge. Established in 2002, MCA Connect has grown into one of the largest US-based solution partners in Microsoft Business Applications and Azure Data & AI / Digital & App Innovation. Our Microsoft Specialties include Finance and Supply Chain, Analytics on Azure, Data Warehouse Migration, and Power Platform. We’re also a fifteen-time Microsoft Partner of the Year and three-time Inc. Best Workplaces award winner." That's about the company, not necessarily role. Could be placed under "About the role"? Usually "About the role" includes description of role and maybe company. We'll include it there. Then Title: Senior Data Scientist (but we must not repeat job title as heading). So we can include title in a
maybe. Location: Virtual, home based office with travel. Description paragraph: "The Senior Data Scientist is responsible for being a partner to team members, a mentor, and directly enabling growth of the Manufacturing Intelligence team at MCA. The Sr. Data Scientist is expected to lead Data Science implementations, drive innovation, upskill and mentor junior teammates, and partner with leadership to drive continuous improvement. Within a project framework, the Sr. Data Scientist is responsible for building client relationships, effectively utilizing Agile project delivery methodologies, implementing scalable and efficient technical architectures to satisfy client requirements, and performing hands on implementations of Microsoft data solutions for MCA Connect’s current and future customers/projects." That's part of responsibilities. Then "Responsibilities" heading followed by bullet list (actually a paragraph with many sentences). We need to convert to
- for any list of duties/requirements/benefits. The responsibilities are many sentences; we can split each sentence into a list item.
Let's list responsibilities sentences:
- Leveraging advanced analytics techniques and tools such as Azure Machine Learning, Azure Open AI, Azure AI Search, Azure Synapse Analytics, Databricks, and Power BI
- Extract insights from large datasets and develop data-driven solutions.
- Lead end-to-end data science projects, collaborate with cross-functional teams, and stay updated with emerging Microsoft technologies.
- Strong problem-solving, programming, and communication skills. (This seems like a qualification but appears in responsibilities block)
- Building predictive models and other analytical solutions.
- Apply statistical and machine learning methods to analyze large, complex data sets.
- Collaborate with various parties to brainstorm creative uses for data.
- Develop complex algorithms and statistical predictive models and determine analytical approaches and modeling techniques to evaluate scenarios and potential future outcomes.
- Perform analyses of structured and unstructured data to solve multiple and complex business problems.
- Deliver actionable guidance and recommendations to stakeholders to help understand nuances of the models built and tactics for improving.
- Act as the subject matter expert on solutions produced by working closely with your peers to help Clients better understand their data.
- Actively participate in the execution and planning of data capture strategies to improve data automation, data quality, and analytics capabilities.
- Run ad-hoc data extracts for requests from diverse partners and automate those needs over time.
- Perform highly complex, technical, and creative predictive analytics projects.
- Manage and prioritize multiple moderate-to-highly complex projects including gathering business requirements, developing project goals and requirements, coordinating project timelines, and communicating project status and deliverables with Clients.
- Stay abreast of architectural/industry changes in related fields.
- Continuously improve Team processes to ensure information is of the highest quality, contributing to the overall effectiveness of the Team.
- Ensure best practices are followed and business objectives are achieved by focusing on process improvements.
- Meet with Clients to determine needs and/or identify problem areas.
- May be responsible for the development of Statement of Work and Proposals which outline professional services required to meet Client requirements.
- Must be open to ~10% travel on an as needed basis.
We need to ensure we don't duplicate content.
Now "Qualifications" heading: list of qualifications.
Qualifications bullet list:
- 7+ years of hands-on experience with data science, AI, and big data.
- Experience with data engineering is a plus.
- Degree in Information Technology or Business Administration or equivalent combination of education and experience
- Experience developing and owning production-level models using various data sets.
- Experience shipping models to production, and ensuring continuous availability via measurements, and tools like statistical process control.
- Ability to translate business needs into technical requirements through active partnerships, collaborating with partners, data scientists and data architects.
- Compelling storytelling through analytics and visualization, getting your point across, and keeping your audience engaged.
- Experience working in Python, R, Scala, Spark, and/or T-SQL.
- Extensive experience connecting to Data Platforms including data lakes, data warehouses, NoSQL databases, and APIs.
- Ability to set up data and experimental platforms.
- Strong critical thinking, active listening, and communication skills to infer business needs, grasp the underlying context, and translate loose direction on analytical projects into concrete solutions.
- Attention to detail and desire for end-to-end ownership of deliverables.
- Experience working on projects involving large volumes of data, preferably in the Manufacturing industry.
- Demonstrates the ability to communicate effectively with technical teams, business stakeholders, and other relevant parties
Note: Some of these overlap with responsibilities (e.g., strong problem-solving etc). We'll keep as given.
Then Pay: "$125,000 - $210,000 a year PLUS Uncapped Supplemental Compensation (Bonus) Plan paid out quarterly"
Benefits: "Why work for MCA Connect? Our Compensation Plan Offers One Of The Best Bonus Structures In The Industry. Along With This We Also Offer a Generous Benefit Package Work/Life Balance with Unlimited Paid Time Off (UPTO) 401k Plan with Company Matching Contribution Monthly Stipend for Home Office Expenses Subsidized Medical, Dental and Vision Coverage Health Savings and Flexible Spending Accounts Company Paid Life and Disability Insurance Training, Certification and Continuing Education Support MCA Connect offers limitless opportunities for personal and professional growth in a stimulating, challenging, and performance-oriented work culture where you can share your ideas and make impactful daily contributions. Our employees are highly motivated and talented individuals dedicated to developing, marketing, and selling products designed to deliver value for mid-market and enterprise-size manufacturing, distribution, and energy companies. We take the time to train our consultants so that they understand the industries we serve and can deliver best practices, proven methodologies, and ongoing industry expertise to our clients."
We need to extract benefits list: Unlimited Paid Time Off (UPTO), 401k Plan with Company Matching Contribution, Monthly Stipend for Home Office Expenses, Subsidized Medical, Dental and Vision Coverage, Health Savings and Flexible Spending Accounts, Company Paid Life and Disability Insurance, Training, Certification and Continuing Education Support. Also mention "Generous Benefit Package". We'll put as list.
Schedule: Not explicitly stated besides travel ~10% as needed. No explicit schedule (e.g., full-time, hours). So we should not create Schedule section unless posting actually states them. It does not state schedule, so omit.
Skills: Could be a section but we have qualifications covering skills. The instruction says prefer a few substantive sections over many thin ones. We could combine Skills into Qualifications or maybe have Skills separate if there is explicit skills list. There is not a separate skills heading; we could treat qualifications as skills. So we may not need a separate Skills section.
Thus sections: About the role (company description + role description), Responsibilities, Qualifications, Benefits, Pay.
We need to ensure we don't include boilerplate like EEO, AI tools, etc. Remove those.
Also remove duplicated lines: The posting had duplicated lines? Not many.
Now produce HTML fragment.
Structure:
About the role
Through passion and deep industry expertise, MCA Connect helps manufacturers succeed by unlocking innovation with actionable business insights. Our strategic solutions, innovation, and industry intelligence help manufacturers gain visibility, improve profitability, and achieve a competitive edge. Established in 2002, MCA Connect has grown into one of the largest US-based solution partners in Microsoft Business Applications and Azure Data & AI / Digital & App Innovation. Our Microsoft Specialties include Finance and Supply Chain, Analytics on Azure, Data Warehouse Migration, and Power Platform. We’re also a fifteen-time Microsoft Partner of the Year and three-time Inc. Best Workplaces award winner.
Title: Senior Data Scientist
Location: Virtual, home based office with travel
The Senior Data Scientist is responsible for being a partner to team members, a mentor, and directly enabling growth of the Manufacturing Intelligence team at MCA. The Sr. Data Scientist is expected to lead Data Science implementations, drive innovation, upskill and mentor junior teammates, and partner with leadership to drive continuous improvement. Within a project framework, the Sr. Data Scientist is responsible for building client relationships, effectively utilizing Agile project delivery methodologies, implementing scalable and efficient technical architectures to satisfy client requirements, and performing hands on implementations of Microsoft data solutions for MCA Connect’s current and future customers/projects.
Then Responsibilities:Responsibilities
- Leveraging advanced analytics techniques and tools such as Azure Machine Learning, Azure Open AI, Azure AI Search, Azure Synapse Analytics, Databricks, and Power BI
- Extract insights from large datasets and develop data-driven solutions.
- Lead end-to-end data science projects, collaborate with cross-functional teams, and stay updated with emerging Microsoft technologies.
- Strong problem-solving, programming, and communication skills.
- Building predictive models and other analytical solutions.
- Apply statistical and machine learning methods to analyze large, complex data sets.
- Collaborate with various parties to brainstorm creative uses for data.
- Develop complex algorithms and statistical predictive models and determine analytical approaches and modeling techniques to evaluate scenarios and potential future outcomes.
- Perform analyses of structured and unstructured data to solve multiple and complex business problems.
- Deliver actionable guidance and recommendations to stakeholders to help understand nuances of the models built and tactics for improving.
- Act as the subject matter expert on solutions produced by working closely with your peers to help Clients better understand their data.
- Actively participate in the execution and planning of data capture strategies to improve data automation, data quality, and analytics capabilities.