Technology

Data Scientist At Ipsos

Ipsos·Lagos, nigeria·Full Time·Contract
TechnologyFull TimeContract
-

Job Purpose

  • The Junior Data Scientist is responsible for the overall quality of the data and sample of the Retails or Consumer audit.
  • Work closely with production and field teams to continuously improve the quality of the ipsos retail and consumer audit products.
  • Ensure that KPIs are produced, shared and tracked across all areas of the retail and consumer audit products.
  • He/She should understand the possible market dynamics that can drive the results of a retais audit. Be able to understand and interpret the data trends. Prepare presentations for internal and possibly telets
  • Estimate universes for existing and future retail audit countries, using both internal and external sources.

Job Scope

  • Individual contributor
  • Works independently within team structure
  • Interact with clients and suppliers

Responsibilities and Deliverables

Universe Estimation:

  • New Countries
  • Existing Countries

Sample:

  • Sample Design
  • Sample Maintenance

Production:

  • Sign off on data
  • Retail Audit System

Training:

  • Train client service teams on methodologies and capabilities of the retails audit/retail execution and census.
  • Train field teams on proper audit procedures and identification of store types.

Requirements

Expertise & Competencies:

  • Good people management and interpersonal
  • Ability to work under pressure, to prioritize, multitask and meet deadlines
  • Proficiency with MS Office package
  • Ability to learn other software packages.
  • Excellent Excel skills and expertin pivot tables.
  • Excellent programming skills, specifically in SQL or SAS

Key skills

BA/BSc/HND
Apply Now
Send your CV along with a cover letter toipsosnigeria@ipsos.com

Please use the job title as the subject line of your email.

At a glance

Company

Ipsos

Location

Lagos, nigeria

Employment

Full Time

Work style

Contract

Experience

Valid until

Not specified

Created

September 22, 2026

More opportunities

Similar roles you might like

Head Of Data Financial Services At Ascentech Services Limited

Ascentech Services Limited

Lagos, nigeria

full-time

Job Summary We are seeking an experienced Head of Data - Financial Services to lead data strategy, analytics, machine learning, credit risk modelling, fraud detection, and data governance for a growing financial services business. The successful candidate will lead a multidisciplinary team of Data Engineers, Data Analysts, and Data Scientists, ensuring data products and models are effectively deployed and aligned with business objectives. Key Responsibilities Lead data strategy, governance, architecture, and data quality initiatives. Develop and deploy credit risk, behavioural scoring, and collections models. Oversee fraud, anomaly detection, and Early Warning Systems (EWS). Drive customer segmentation, propensity, churn, cross-sell, and growth analytics. Ensure AI/ML models are deployed, monitored, and optimized in production. Partner with Risk, Product, Commercial, Engineering, Finance, and Compliance teams. Lead, mentor, and develop the data team while establishing strong technical and MLOps standards. Requirements Bachelor\'s degree in Statistics, Mathematics, Computer Science, Engineering, Economics, or related field. 10+ years\' experience in Data Science, Analytics, or Data Engineering. 4+ years\' experience leading multidisciplinary data teams, preferably in banking, lending, or fintech. Proven experience in credit risk modelling, fraud analytics, and financial transaction data. Strong knowledge of SQL, Python, Snowflake, Power BI, PySpark, Machine Learning, MLOps, and Cloud platforms. Experience with data governance, model risk management, and financial services regulations. Experience in SME, retail, or informal-sector lending is an advantage.

3 days ago

Data Scientist, Risk At Flutterwave

Flutterwave

Lagos, nigeria

full-time

The Role Flutterwave is seeking a highly analytical and technically driven Data Scientist to join our dynamic Risk team. As a fintech company operating in a rapidly evolving landscape, detecting anomalies and mitigating risk at scale is paramount to our success. In this role, you will contribute to the transition of our risk capabilities from reactive analytics to proactive predictive modeling. You will be responsible for designing, training, and deploying robust machine learning models to detect fraud, assess risk, and protect our infrastructure. The ideal candidate possesses a deep understanding of ML algorithms, strong engineering fundamentals, and the ability to translate complex data into scalable, automated risk solutions. Responsibilities: Model Development: Design, develop, and optimize data-driven algorithms and machine learning models specifically focused on fraud detection, transaction monitoring, and risk mitigation. Feature Engineering: Collect, clean, and analyze massive transactional datasets from multiple sources to identify predictive features and emerging fraud trends. Deployment & Monitoring: Partner directly with the Engineering and MLOps teams to deploy models into production environments. Monitor model performance, track data drift, and retrain models to ensure ongoing accuracy. Risk Strategy Optimization: Translate complex model outputs into actionable business rules and strategies. Work with the compliance and operations teams to balance fraud prevention with user friction. Data Pipeline Validation: Collaborate with cross-functional teams to validate data accuracy, integrity, and consistency across all machine learning and risk pipelines. Documentation & Culture: Maintain accurate, up-to-date documentation of model architectures, training datasets, and algorithmic decisions to support internal and external audits and regulatory requirements. Required Competency and Skillset: Education: Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field. Experience: 3 - 5 years of proven experience as a Data Scientist building and deploying machine learning models in a production environment. Industry Knowledge: Experience working in the Fintech or payments industry is required. Bonus/Preferred: Direct experience building fraud detection, credit risk, or anti-money laundering (AML) models is a plus. Technical Stack: Strong programming skills in Python and SQL. Deep proficiency with ML libraries (e.g., Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch). Data Tools: Experience working with cloud data warehouses (e.g., Redshift, Snowflake, BigQuery). Soft Skills: Exceptional problem-solving skills and the ability to communicate complex, algorithmic concepts clearly to non-technical stakeholders (Legal, Finance, Operations).

4 days ago

Senior Data Scientist At Vennote Technologies Limited

Vennote Technologies Limited

Lagos, nigeria

full-time

We are looking for you if you: Have experience building Descriptive/Predictive/Prescriptive analytical data model. Have experience identifying, sourcing, cleaning, and preparing appropriate datasets that can be used to train the chatbot, such as conversational data, product manuals, FAQs, and customer support transcripts Have extensive experience extracting meaningful features from the data that can be used by the machine learning models to improve chatbot performance. Can select machine learning algorithms (e.g., recurrent neural networks, transformers) that are well-suited for natural language processing tasks. Can develop and train the chatbot models using prepared data and can tune hyperparameters to optimize performance. Have experience assessing chatbot's ability to understand user queries, provide relevant responses, and engage in meaningful conversations. Can work with BAs / UX / Product Owner to complete the writing of user stories and documenting analytics requirements Validate incoming data on development and production tables as testing takes place. Write SQL queries in BigQuery to extract the data required for each feature. Explore data analysis in Python / SQL Create scheduled queries in BigQuery to continuously index data Connect data sources to a Data Studio dashboard and create visualizations of key data Monitor dashboards and providing observations / insight back to the product team Monitor Google cloud metrics Build ETL pipelines to import relevant data into the data warehouse Work closely with project management teams to successfully monitor progress of initiatives and deliveries Provide Solution document, technical document and detailed specifications for proposed solutions Have an agile product mentality balancing between finding solutions that deliver high precision/recall, over simpler approaches that can give value to customers in shorter intervals. Demonstrate passion, excitement, and real interest in what we do while being respectful of others and fun to work with Get involved in all stages of ML lifecycle You are: A Data Scientist with over 7 years+ experience in building Descriptive/ Predictive/ Prescriptive analytical data models. Advanced proficiency in Python or R, SQL, Jupyter, Pandas, Dask. Deep understanding of machine learning algorithms, both supervised, unsupervised learning and deep learning (linear regression, decision trees, random forests, clustering, dimensionality reduction, neural networks, recurrent neural networks, transformers etc) Experience with popular chatbot frameworks like Rasa, Dialogflow, or Amazon Lex Proficiency in techniques like tokenization, stemming, lemmatization, part-of-speech tagging, named entity recognition, and sentiment analysis. Have core competencies in Google data Studio and Google Cloud platforms like Big Query, PubSub, Data flow Holding a degree in software engineering, Computer Science, or similar field Positive outlook in meeting challenges and working to a high level Highly knowledgeable and efficient in standards and documentation Self-motivated, proactive with demonstrated creative and critical thinking capabilities Quick to adjust inside a rapid-changing environment

5 days ago

Group Head - Artificial Intelligence & Digital Transformation At Dangote

Dangote

Lagos, nigeria

full-time

Job Summary: The Group Head: AI & Digital Transformation is responsible for defining and executing the Group's enterprise-wide Artificial Intelligence and Digitisation strategy, translating emerging technologies into measurable improvements in operational performance, reliability, productivity, safety, risk management and commercial value. The role will identify, prioritise and scale high-value AI and digital use cases across the Group's industrial portfolio, including refining, petrochemicals, fertiliser, cement, manufacturing, mining/raw materials, logistics, transport, supply chain and corporate functions. The role will lead the development and deployment of AI-enabled solutions including machine learning, predictive and prescriptive analytics, computer vision, optimisation algorithms, generative AI, intelligent automation, digital twins and advanced decision-support systems. The Group Head will operate at the intersection of business, industrial operations, data, technology and AI, working closely with the GCRO, GCIO, business CEOs/MDs, plant leadership and functional executives. The role will ensure that AI and digitisation investments move beyond experimentation to production-scale solutions with defined business outcomes, appropriate governance and measurable return on investment. AI & Digitisation is a centre-led Group capability, with execution embedded across business units and operating environments Qualifications & Experience Bachelor's degree in Artificial Intelligence, Computer Science, Data Science, Machine Learning, Software Engineering, Computer Engineering, Electrical/Electronic Engineering, Robotics, Mathematics, Statistics, Computational Science or related quantitative discipline. Master's degree in Artificial Intelligence, Machine Learning, Computer Science, Data Science, Engineering, Robotics or a related advanced technology discipline strongly preferred. Executive education in technology, innovation, digital transformation or business strategy would be advantageous. 10+ years' relevant technology/data/AI experience, with meaningful leadership responsibility and evidence of deploying advanced technology into production environments. Demonstrable experience taking AI solutions from problem definition and data engineering through model development, deployment, monitoring and value realisation. Experience in industrial, energy, oil & gas, manufacturing, utilities, mining, logistics, infrastructure or other asset-intensive environments strongly preferred. Experience building or leading multidisciplinary teams comprising data scientists, ML engineers, data engineers, software engineers, product specialists and business/industrial SMEs. Demonstrable portfolio of successfully deployed AI/digital products with quantifiable operational or financial outcomes Key Requirements Advanced understanding of machine learning, deep learning, generative AI, large language models and advanced analytics. Strong understanding of Python, SQL and modern AI/ML development environments; technical credibility sufficient to interrogate models, architectures and algorithms. Understanding of time-series modelling, forecasting, optimisation, anomaly detection and predictive modelling. Knowledge of computer vision, NLP, intelligent automation and agentic AI systems. Understanding of digital twins, IoT, edge computing, sensors and industrial data environments. Strong understanding of MLOps, model deployment, APIs, cloud platforms, data pipelines and model monitoring. Ability to apply AI to rotating equipment, process plants, production optimisation, asset integrity, reliability and industrial risk. Ability to translate complex industrial and business problems into mathematical, analytical and AI-solvable problems. Strong commercial orientation with ability to quantify ROI, productivity gains, avoided losses, downtime reduction and margin improvement. Strong understanding of AI governance, model risk, data privacy, cybersecurity and responsible AI. Exceptional strategic thinking, innovation, stakeholder influence and executive communication. Ability to challenge technology hype and distinguish between automation, analytics, digitisation and genuine AI applications.a

7 days ago

Data Scientist (fraud) At Moniepoint Inc.

Moniepoint Inc.

Lagos, nigeria

full-time

About this role We're looking for a Data Scientist to sit at the heart of how we fight fraud building the models, experiments, and detection systems that protect millions of customers and merchants across our platform. This is a high-impact role at the intersection of machine learning, product, and engineering, where your work will directly shape how Moniepoint detects and responds to emerging fraud threats. You are a data-driven, intellectually curious Data Scientist who is energized by hard problems in fraud and financial crime. You'll prototype and ship ML models, design experiments, and uncover new fraud signals across our ecosystem partnering closely with engineers, product managers, and analysts to turn your work into production-grade systems. Responsibilities Prototype, evaluate, and help produce machine learning models for fraud detection; own their ongoing monitoring and retraining cycles. Design and run experiments to measure the impact of fraud interventions, balancing customer experience against loss reduction. Size fraud typologies across our product lines to inform prioritization and investment decisions. Build and maintain anomaly detection systems to surface novel fraud vectors before they scale. Work closely with fraud operations, engineers, product managers, and data analysts to translate model outputs into real-world mitigations. Experience & Background A strong foundation in statistics with a degree in a quantitative field (Statistics, Mathematics, Engineering, Computer Science, or similar). 3+ years of experience in data science, decision science, or risk analytics within fraud, payments, or financial crime. Hands-on experience building and deploying machine learning models in a production environment. Fraud, risk, or financial services experience is a strong plus. Solid grounding in data science fundamentals: experimentation, statistical inference, model evaluation, and feature engineering. Comfort working in fast-paced, cross-functional teams with high ownership expectations. Skills & Competencies: Proficiency in Python and SQL; comfort working across the full model development lifecycle. An investigative instinct you enjoy digging into data to find patterns others miss. The ability to communicate technical findings clearly to non-technical stakeholders and translate insights into action. What Success Looks Like in This Role: Production-grade ML models and anomaly detection systems that effectively surface and mitigate novel fraud vectors before they scale. Well-designed experiments that successfully balance customer experience against fraud loss reduction. Clear sizing of fraud typologies that effectively drives product prioritization and strategic investment decisions. Seamless cross-functional alignment where technical model outputs are consistently translated into real-world fraud mitigations. Why Join Us? Culture: We put our people first and prioritize the well-being of every team member. We've built a company where all opinions carry weight and where all voices are heard. We value and respect each other and always look out for one another. Above all, we are human. Learning: We have a learning and development-focused environment with an emphasis on knowledge sharing, training, and regular internal technical talks. Compensation: You'll receive an attractive salary, pension, health insurance, annual bonus, plus other benefits.

11 days ago