Senior Presales Solution Architect
1 week ago
The Role
Job Description A Data Science Solution Architect plays a crucial role in helping businesses adopt data-driven solutions by leveraging advanced analytics, machine learning, and AI technologies. They work closely with sales teams to understand client needs and demonstrate how data science solutions can solve business problems. Key Responsibilities: Customer Engagement: - Engage with potential customers to understand their business challenges, data needs, and goals. - Gather requirements from customers to assess how data science solutions can be applied to solve their problems. - Conduct product demonstrations, technical presentations, and workshops to showcase the capabilities of data science and AI solutions. - Build relationships with key customer stakeholders, including data scientists, business analysts, and decision-makers. Solution Design: - Design data science and machine learning solutions tailored to customer-specific use cases, leveraging tools such as Python, R, TensorFlow, and cloud-based platforms (AWS, Azure, GCP). - Create prototypes, proof of concepts (PoCs), and demonstrations to validate the proposed solutions' impact on customer problems. - Work with structured and unstructured data to build predictive models, data pipelines, and analytical dashboards. - Ensure the proposed solutions are scalable, secure, and integrate seamlessly with existing systems. Product Expertise: - Maintain a deep understanding of data science methodologies, tools, and platforms. - Stay up-to-date on the latest trends and advancements in AI, machine learning, big data, and data analytics. - Provide guidance on selecting appropriate algorithms, models, and statistical techniques to solve specific business problems. - Evaluate customer data sources to determine how they can be used for analytics and insights. - Strong knowledge of major database vendors such as Microsoft SQL, Mongo DB, Couchbase, etc. - Experience using Visio to design solutions. - Experience building High-Level Designs (HLD). - Experience generating the Bill of Quantities (BOQ) and licenses for the database vendors.
**Requirements**:
- Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. - 10-15+ years of experience in data science, machine learning, AI, or analytics roles, ideally with a focus on pre-sales or customer-facing engagements. - Proficiency in data science programming languages and tools such as Python, R, SQL, TensorFlow, PyTorch, and cloud-based AI platforms (AWS, GCP, Azure). - Hands-on experience building machine learning models, data pipelines, and visualizations using tools like Tableau, Power BI, or Jupyter notebooks. - Strong understanding of statistical analysis, data mining, predictive modeling, and natural language processing (NLP). - Excellent communication and presentation skills, with the ability to explain complex technical concepts to non-technical audiences. - Strong business acumen and the ability to translate customer challenges into actionable data science solutions. Certifications (preferred): - Certified Data Scientist (CDS) or other recognized data science certifications. - AWS Certified Machine Learning - Specialty, Google Professional Machine Learning Engineer, or Azure AI Engineer certifications. - Professional certifications in data visualization (e.g., Qlik, Tableau, or Power BI).
About the company
Giza Systems, a leading systems integrator in the MEA region, designs and deploys industry-specific technology solutions for asset-intensive industries such as the telecoms, utilities, oil and gas, hospitality and real estate among other market sectors. We help our clients streamline their operations and businesses through our portfolio of solutions, managed services, and consultancy practice. Our team of 1000 professionals are spread throughout the region with anchor offices in Cairo, Riyadh, Dubai, Doha, Nairobi, Dar-es-Salaam, Abuja, Kampala and New Jersey, allowing us to service an ever-increasing client base in over 40 countries.
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