Specialist Data Scientist

for paragraphs,

for section headers,

• for list items. No CSS classes, no inline styles, no
  , no
  . Must preserve all original content word-for-word, no addition/removal. Must convert the given raw HTML (with inline styles etc.) into proper semantic HTML. We need to parse the job description text and produce HTML. Given the raw input includes many

  tags with style attributes, etc. We need to strip those and produce plain text content, preserving content exactly. We need to identify sections: There's a title "Let's Write Africa's Story Together!" (emphasized). Then a paragraph about Old Mutual. Then blank paragraphs. Then "Job Description" bold. Then paragraphs describing role. Then sections: "Key Result Areas" bold, then a

  list. Then "Data Analysis" bold, then list. Then "Advanced Analytics" bold, list. Then "Team Effectiveness" bold, list. Then "Personal Effectiveness" bold, list. Then "Qualifications and experience:" bold, list. Then "Skills" bold, then a line of skills text. Then "Competencies" bold, then list of competencies each preceded by
  weird. Then "Education" bold, then line. Then "Closing Date" bold, then date. Then italic bold statement about appointment. Then italic emphasis-2 "The Old Mutual Story!" line. We need to convert bold section headers to

  . For list items, we need

  • . For paragraphs,

    . Must preserve content exactly, including punctuation and spaces? We should keep the text as is, but remove HTML tags and extra whitespace? The instruction: "Preserve ALL original content word-for-word — do not add, remove, summarise, or rephrase". That means we must keep the exact textual content, but we can change formatting to HTML tags. We must not remove any characters from the content. However, we can drop the original HTML tags (like

    ) because they are not content; they are formatting. The content is the text inside. We must ensure we don't lose any spaces or line breaks? Probably we can normalize whitespace but must keep the words same. Safer to keep the text exactly as appears between tags, preserving internal spaces. We need to parse the given input string and extract text nodes. Let's manually extract content. I'll rewrite the content as plain text: Start: Let's Write Africa's Story Together! Old Mutual is a firm believer in the African opportunity and our diverse talent reflects this. (blank lines) Job Description You will form part of the Mass Foundation Cluster which is one of the largest customer-facing business segments in Old Mutual. Our vision is to be our customers' most trusted financial partner, passionate about helping them achieve their lifetime goals. Our mission is to enable positive futures for our customers, by bridging the gap between their resources and their financial service’s needs. Our primary goal is to facilitate access to financial solutions for our customers and to keep pace with their changing needs. Our target market is the low and emerging-middle income segment. This role is responsible for turning data into actionable insights for business partners. The role focusses on delivering insight and will also be responsible for creating end to end solutions which includes problem definition, data acquisition, data exploration and visualization, experimenting with statistical and machine learning methods when required, implementing statistical models or solutions where appropriate and providing business with the required insights. The incumbent is individually accountable for achieving results over periods of 3 to 12 months. Key Result Areas - Turning data into actionable insight for our business partners is what the Customer Experience Analytics team does. We are relentlessly curious and use our advanced analytical skills, tools and platforms to present data as information and create analytical solutions that are interactive and easy to understand. - To deliver insights you will be responsible for creating end to end solutions which includes problem definition, data acquisition, data exploration and visualization, experimenting with statistical methods when required, implementing statistical models or solutions where appropriate and providing business with the required insights. - The ideal candidate will have sound business acumen, analytical thinking skills with modelling experience and the ability to communicate key insights to business in an effective and easy to understand manner. Data Analysis - Database structures. - Data extraction, manipulation and maintenance (SQL, R, SAS, Excel, Python). - Data visualization (PowerBI, Tableau). Advanced Analytics - Statistical analysis and predictive modelling (R, Data Robot, SAS Enterprise Miner, Alteryx, SPSS, Python). - Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks. - Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications. Team Effectiveness - Coaches/ Mentors junior data scientists in the areas of machine learning, statistical analysis and building predictive models. - Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions through the use of advanced analytics. - Develop advanced analytics data driven solutions to difficult business problems. Personal Effectiveness - Accepts and lives the company values. - Accountable for service delivery through own efforts. - Collaborates effectively with others to achieve personal results. - Individually accountable for managing own time, tasks and output quality over periods of up to a year. - Makes increased contributions by broadening individual skills. - Desire to continually research and learn (new technologies, tools, techniques, platforms). - Excellent communicating and presenting skills (written and verbal) - Ability to work under pressure in a fast paced and dynamic environment. - Great at building individual, team and partner relationships. Qualifications and experience: - Relevant tertiary/post- graduate qualification in statistics or another advanced analytics related field. - 5 - 8 years’ work experience of which at least 5 years must be in an advanced analytics type role with model building experience. - End to end experience and understanding of data analytics. - Database structures. - Data extraction, manipulation and maintenance (SQL, R, SAS, Excel, Python). - Statistical analysis and predictive modelling (R, Data Robot, SAS Enterprise Miner, Alteryx, SPSS, Python). - Data visualization (PowerBI, Tableau). - Some experience with R, AWS, Amazon SageMaker or Azure Machine Learning will be beneficial. - Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks. - Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications. Skills Data Analysis, Data Structures, Data Visualization, Model Building, Predictive Modeling, Statistical Analysis Competencies Business Insight Cultivates Innovation Drives Results Manages Ambiguity Manages Complexity Plans and Aligns Situational Adaptability Strategic Mindset Education NQF Level 7 - Degree, Advance Diploma or Postgraduate Certificate or equivalent Closing Date 04 September 2026 , 23:59 The appointment will be made from the designated group in line with the Employment Equity Plan of Old Mutual South Africa and the specific business unit in question. The Old Mutual Story! Note: The last line appears as italic with emphasis-2 and bold inside. The content: "The Old Mutual Story!" (with italic and bold). We must preserve that text exactly: "The Old Mutual Story!" (including the exclamation). The surrounding italic and bold tags are formatting, not content, so we just output the text. Now we need to wrap in HTML: Use

    for introductory paragraphs and general text. Use

    for section headers (e.g., Responsibilities, Qualifications, Requirements, Education, Experience, Skills, How to Apply, Key Duties, About the Role). Here we have sections: Job Description (maybe treat as

    ?), Key Result Areas, Data Analysis, Advanced Analytics, Team Effectiveness, Personal Effectiveness, Qualifications and experience:, Skills, Competencies, Education, Closing Date. Also there is the initial title "Let's Write Africa's Story Together!" and the Old Mutual paragraph. Those could be

    . The instruction: Use

    for section headers (e.g. Responsibilities, Qualifications, Requirements, Education, Experience, Skills, How to Apply, Key Duties, About the Role). It doesn't forbid using

    for other section headers like "Job Description", "Key Result Areas", etc. Likely we should treat any bold heading as

    . Also the initial title maybe not a section header; could be

    with emphasis? But we cannot add styling; we just output as

    . We must not add any extra content. So we will output:

    Let's Write Africa's Story Together!

    Old Mutual is a firm believer in the African opportunity and our diverse talent reflects this.

    Then maybe a blank line? We can just have

    for empty? But we should not add empty paragraphs if they were just blank lines in original? The original had several

    empty paragraphs. Those are empty content; we could either omit them (since they have no content) or keep as

    . The instruction: preserve ALL original content word-for-word. Empty paragraphs have no content, but they are part of original. Should we preserve them? Probably we can ignore empty tags because they have no textual content; preserving them would be adding

    which wasn't in the original visible text? Actually they were present as empty

    tags. If we omit them, we are removing original content (the empty paragraph tags). However, the instruction likely refers to textual content, not tags. It says "Preserve ALL original content word-for-word — do not add, remove, summarise, or rephrase". The content is the text; tags are not content. So we can drop empty paragraphs. Thus after the two paragraphs, we have "Job Description" heading. We'll make

    Job Description

    . Then the paragraph(s) following. After that heading, we have the paragraph describing role (two paragraphs? Actually there is a paragraph then a blank line then another paragraph? In original there is a paragraph then
    
    then another paragraph. The content is: "You will form part...". Then after that there is a blank line then "This role is responsible...". Actually the original: after the description paragraph, there is
    
    then "This role is responsible...". So it's two paragraphs separated by a line break. We'll treat as two

    elements. Thus:

    You will form part of the Mass Foundation Cluster which is one of the largest customer-facing business segments in Old Mutual. Our vision is to be our customers' most trusted financial partner, passionate about helping them achieve their lifetime goals. Our mission is to enable positive futures for our customers, by bridging the gap between their resources and their financial service’s needs. Our primary goal is to facilitate access to financial solutions for our customers and to keep pace with their changing needs. Our target market is the low and emerging-middle income segment.

    This role is responsible for turning data into actionable insights for business partners. The role focusses on delivering insight and will also be responsible for creating end to end solutions which includes problem definition, data acquisition, data exploration and visualization, experimenting with statistical and machine learning methods when required, implementing statistical models or solutions where appropriate and providing business with the required insights. The incumbent is individually accountable for achieving results over periods of 3 to 12 months.

    Then heading: Key Result Areas ->

    Key Result Areas

    . Then list items: each
  • with paragraph inside? In original each
  • had a

    inside. We need to output

    • ...
    . The content inside each
  • is a paragraph (the text). We can just put the text directly inside
  • without extra

    . The instruction: Use

    • for list items (duties, requirements, qualifications, skills). It doesn't forbid having

      inside

    • , but we can keep it simple: just the text. Thus:
      • Turning data into actionable insight for our business partners is what the Customer Experience Analytics team does. We are relentlessly curious and use our advanced analytical skills, tools and platforms to present data as information and create analytical solutions that are interactive and easy to understand.
      • To deliver insights you will be responsible for creating end to end solutions which includes problem definition, data acquisition, data exploration and visualization, experimenting with statistical methods when required, implementing statistical models or solutions where appropriate and providing business with the required insights.
      • The ideal candidate will have sound business acumen, analytical thinking skills with modelling experience and the ability to communicate key insights to business in an effective and easy to understand manner.
      Then heading: Data Analysis ->

      Data Analysis

      . Then list. Similarly for Advanced Analytics, Team Effectiveness, Personal Effectiveness, Qualifications and experience:. Note: "Qualifications and experience:" includes colon. We'll keep as heading text exactly: "Qualifications and experience:" (including colon). Use

      . Then after that list, we have "Skills" heading:

      Skills

      . Then the skills line: "Data Analysis, Data Structures, Data Visualization, Model Building, Predictive Modeling, Statistical Analysis". This is not a list; it's a paragraph. So we wrap in

      . Then "Competencies" heading:

      Competencies

      . Then each competency line appears as separate lines but originally each preceded by
      weird. The content lines: Business Insight, Cultivates Innovation, Drives Results, Manages Ambiguity, Manages Complexity, Plans and Aligns, Situational Adaptability, Strategic Mindset. These are separate items; likely they should be a list. The instruction: Use
      • for list items (duties, requirements, qualifications, skills). Competencies could be considered a list. We'll treat as
        • each. Then "Education" heading:

          Education

          . Then paragraph: "NQF Level 7 - Degree, Advance Diploma or Postgraduate Certificate or equivalent". Then "Closing Date" heading:

          Closing Date

          . Then paragraph: "04 September 2026 , 23:59". Note there is a space before comma? Original: "04 September 2026 , 23:59". We'll keep exactly. Then the italic bold statement: "The appointment will be made from the designated group in line with the Employment Equity Plan of Old Mutual South Africa and the specific business unit in question." The content: "The appointment will be made from the designated group in line with the Employment Equity Plan of Old Mutual South Africa and the specific business unit in question." We'll wrap in

          (maybe

          with italic and bold? But we cannot add styling; we just output the text as

          ...

          . The instruction says preserve content word-for-word; we can drop the tags as they are formatting. So just

          The appointment will be made from the designated group in line with the Employment Equity Plan of Old Mutual South Africa and the specific business unit in question.

          Then final line: "The Old Mutual Story!" Content: "The Old Mutual Story!" We'll output

          The Old Mutual Story!

          . Make sure no extra spaces at start/end. Now produce HTML string. Let's craft:

          Let's Write Africa's Story Together!

          Old Mutual is a firm believer in the African opportunity and our diverse talent reflects this.

          Job Description

          You will form part of the Mass Foundation Cluster which is one of the largest customer-facing business segments in Old Mutual. Our vision is to be our customers' most trusted financial partner, passionate about helping them achieve their lifetime goals. Our mission is to enable positive futures for our customers, by bridging the gap between their resources and their financial service’s needs. Our primary goal is to facilitate access to financial solutions for our customers and to keep pace with their changing needs. Our target market is the low and emerging-middle income segment.

          This role is responsible for turning data into actionable insights for business partners. The role focusses on delivering insight and will also be responsible for creating end to end solutions which includes problem definition, data acquisition, data exploration and visualization, experimenting with statistical and machine learning methods when required, implementing statistical models or solutions where appropriate and providing business with the required insights. The incumbent is individually accountable for achieving results over periods of 3 to 12 months.

          Key Result Areas

          • Turning data into actionable insight for our business partners is what the Customer Experience Analytics team does. We are relentlessly curious and use our advanced analytical skills, tools and platforms to present data as information and create analytical solutions that are interactive and easy to understand.
          • To deliver insights you will be responsible for creating end to end solutions which includes problem definition, data acquisition, data exploration and visualization, experimenting with statistical methods when required, implementing statistical models or solutions where appropriate and providing business with the required insights.
          • The ideal candidate will have sound business acumen, analytical thinking skills with modelling experience and the ability to communicate key insights to business in an effective and easy to understand manner.

          Data Analysis

          • Database structures.
          • Data extraction, manipulation and maintenance (SQL, R, SAS, Excel, Python).
          • Data visualization (PowerBI, Tableau).

          Advanced Analytics

          • Statistical analysis and predictive modelling (R, Data Robot, SAS Enterprise Miner, Alteryx, SPSS, Python).
          • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
          • Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.