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MOBIZ

<div>About Mobiz

Mobiz is an information technology firm, and we do three things exceptionally well: cloud and network automation, digital transformation, and data protection. Double-digit growth year-over-year has driven us to grow our team and ensure we stay ahead of the curve. 
What Can You Expect?

Every day at Mobiz we work with a deep sense of purpose. We continuously innovate. Our mission is to empower our clients to do more through transformation. You&rsquo;ll work in a collaborative environment alongside highly talented people that improve client operations and exceed expectations. We strive to simplify technology challenges, and no less. 


Who Are We Looking For?

We are looking for a skilled and motivated Data Engineer to join our growing data team. In this role, you will be responsible for designing, building, and operating scalable, secure, and high-performing data solutions that enable analytics, reporting, and data-driven decision-making across the organization. You will collaborate closely with cross-functional teams and leverage modern cloud technologies to transform raw data into trusted, accessible insights. 


Key Responsibilities
<ul>
<li>Design, build, test, deploy, and maintain robust data pipelines using Databricks, Azure Data Factory, and Microsoft Fabric, supporting both batch and streaming workloads.</li>
<li>Ingest, integrate, and harmonize data from multiple cloud and on-premises sources to deliver a unified, reliable, and trusted data view for analytics and reporting.</li>
<li>Use Python and SQL to clean, transform, validate, and model data to meet business, reporting, and analytical requirements.</li>
<li>Contribute to data architecture and platform design decisions, ensuring solutions are scalable, secure, cost-effective, and aligned with long-term business and technical strategy.</li>
<li>Build and maintain metadata-driven pipelines that gracefully adapt to schema changes, new data sources, and evolving ingestion patterns.</li>
<li>Proactively identify data quality issues, pipeline failures, and performance bottlenecks; perform root-cause analysis and implement durable, long-term solutions.</li>
<li>Continuously optimize data pipeline performance, reliability, and scalability to ensure fast, consistent, and dependable access to data.</li>
<li>Implement automated data quality checks, validation rules, and monitoring dashboards to ensure accuracy, consistency, and trust in delivered datasets.</li>
<li>Monitor, analyze, and manage data pipeline and compute costs across Azure, Databricks, and Microsoft Fabric, recommending and implementing cost-optimization strategies without compromising performance or availability.</li>
<li>Automate routine and repetitive data engineering tasks, applying best practices for CI/CD, deployment, testing, and operational support.</li>
<li>Design, implement, and maintain secure data access controls using role-based access, audits, and governance best practices to ensure appropriate data security and compliance.</li>
<li>Maintain clear, accurate, and up-to-date technical documentation, and provide ongoing operational support as data platforms and business needs evolve.</li>
<li>Stay current with emerging data engineering technologies, cloud services, and analytics best practices, and evaluate new tools through proofs of concept when appropriate.</li>
<li>Collaborate closely with product managers, software engineers, analysts, and operations teams to embed high-quality data solutions into core business processes.</li>
<li>Promote and enforce data engineering best practices including version control, modular and reusable pipeline design, documentation standards, and scalable architecture patterns.</li>
<li>Enable and support data democratization by providing well-modeled, secure, and self-service-ready datasets through Power BI, Microsoft Fabric, or custom analytics solutions.</li>
<li>Deliver measurable outcomes such as highly reliable pipelines with minimal manual intervention, on-time pipeline execution within service windows, rapid resolution of critical incidents, and consistently high data quality.</li>
<li>Ensure reusable and standardized pipeline components are leveraged across teams to accelerate delivery, improve consistency, and reduce operational overhead.</li>
<li>Provide stakeholders with reliable, well-modeled datasets that can be confidently explored without requiring ongoing engineering support.</li>
<li>Ensure all data solutions comply with organizational security, access, and governance policies while maintaining positive feedback from internal and external users.</li></ul> 


 Required Skills &amp; Qualifications 
<ul>
<li>3&ndash;5 years of hands-on experience in data engineering or related roles, with proven experience building, supporting, and operating production-grade data pipelines.</li>
<li>Strong hands-on experience with Databricks, including development, optimization, and operational support of Databricks-based data solutions.</li>
<li>Proven experience with Azure Data Factory (ADF) and Microsoft Fabric for data ingestion, orchestration, and transformation.</li>
<li>Advanced proficiency in SQL for querying, data modeling, performance tuning, and troubleshooting.</li>
<li>Strong programming skills in Python for data transformation, automation, scripting, and pipeline development.</li>
<li>Hands-on experience with Microsoft Azure cloud services related to data storage, compute, networking, and security.</li>
<li>Solid understanding of ETL/ELT patterns, data integration strategies, and real-time or near-real-time data processing concepts.</li>
<li>Experience designing metadata-driven and reusable pipeline components that scale across multiple teams and projects.</li>
<li>Strong understanding of data governance, data quality frameworks, monitoring practices, and automated validation techniques.</li>
<li>Experience supporting high-availability data platforms with SLAs, monitoring, alerting, and incident response processes.</li>
<li>Demonstrated ability to optimize cloud compute usage and manage costs efficiently across Azure, Databricks, and Microsoft Fabric.</li>
<li>Experience using version control systems such as Git and following modern software engineering best practices.</li>
<li>Ability to design secure, scalable, and cost-effective data architectures aligned with business needs.</li>
<li>Strong analytical, problem-solving, and troubleshooting skills with a proactive and ownership-driven mindset.</li>
<li>Excellent communication skills, with the ability to explain complex data concepts to both technical and non-technical stakeholders.</li>
<li>Ability to work independently as well as collaboratively in cross-functional team environments.</li>
<li>High attention to detail with a strong focus on data accuracy, reliability, and documentation quality.</li>
<li>Bachelor&rsquo;s degree in Computer Science, Engineering, or a related field.</li>
<li>Relevant certifications are required or strongly preferred, including Databricks Certified Data Engineer, Microsoft Certified: Azure Data Engineer Associate, and Microsoft Certified: Fabric Data Engineer Associate.</li></ul> 


What We Offer
<ul>
<li>A team of bright, hard-working, and innovative people that will contribute to your growth.</li>
<li>Competitive Salary and comprehensive benefits plan.</li>
<li>A dynamic and collaborative work environment with opportunity to work with cutting-edge technology and innovative solutions.</li></ul> 


Other 
This is a full-time office based job in Islamabad, Pakistan. 
Closing

Mobiz embraces diversity and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. The more inclusive we are, the better we are. We are an equal opportunity employer.

We thank all applicants for their interest. We will contact you if you are selected to move forward in the recruiting process.</div>

Data Engineer

Confiz

<div>We are looking for a Principal Data Engineer, responsible for leading data engineering teams. He/she will provide guidance, mentorship, and technical expertise to team members, fostering a collaborative and innovative work environment. Collaboration is key for a Principal Data Engineer as he/she will work closely with stakeholders to define project requirements. Therefore, a commitment to collaborative problem solving, sophisticated design, and the creation of quality products is essential.
Responsibilities:
<ul><li>Collaborate with Big Data Solution Architects to design, prototype, implement, and optimize data ingestion pipelines so that data is shared effectively across various business systems.</li>
<li>Build &amp; maintain ETL/ELT Ingestion pipelines and analytics solutions using cloud technologies.</li>
<li>Ensure the design, code and procedural aspects of solution are production ready, in terms of operational, security and compliance standards.</li>
<li>Implement monitoring and logging solutions to ensure data pipelines run smoothly and issues are promptly identified.</li>
<li>Ensure all data processing complies with security policies and data protection regulations.</li>
<li>Takes the lead in ensuring that data is accurate, consistent, and reliable.</li>
<li>Participate in day-to-day project and production delivery status meetings, and provide technical support for faster resolution of issues.</li>
<li>Work with Stakeholders and Understand their Requirements</li>
</ul>
Qualifications
<ul><li>Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent work experience).</li>
<li>5+ years of overall experience in data engineering &amp; data warehousing domain focusing on building large scale data pipelines</li>
<li>3+ years' experience in Big Data Distributed systems such as Microsoft Fabric, Databricks, AWS EMR</li>
<li>Strong experience in developing data pipelines using PySpark and ETL tools like ADF, Talend</li>
<li>Proficiency in Python &amp; SQL</li>
<li>Solid understanding of data modelling, ETL processes, and best practices in data warehousing domain</li>
<li>Strong problems solving and critical thinking skills</li>
<li>Experience of writing effective and maintainable unit and integration tests for ingestion pipelines.</li>
<li>Working knowledge of CI/CD pipelines for deployments.</li>
<li>Working knowledge of using code version control repositories like Git.</li>
<li>Knowledge of designing, developing, and managing Kafka-based data pipelines</li>
<li>Knowledge of security best practices including encryption of sensitive data, encryption at rest and encryption in flight.</li>
</ul>
We have an amazing team of 700+ individuals working on highly innovative enterprise projects &amp; products. Our customer base includes Fortune 100 retail and CPG companies, leading store chains, fast-growth fintech, and multiple Silicon Valley startups.
What makes Confiz stand out is our focus on processes and culture. Confiz is ISO 9001:2015 (QMS), ISO 27001:2022 (ISMS), ISO 20000-1:2018 (ITSM), ISO 14001:2015 (EMS), ISO 45001:2018 (OHSMS) Certified. We have a vibrant culture of learning via collaboration and making workplace fun.
People who work with us work with cutting-edge technologies while contributing success to the company as well as to themselves.
To know more about Confiz Limited, visit: https://www.linkedin.com/company/confiz-pakistan/</div>

Principal Data Engineer

S&P Global

About the Role: 
Grade Level (for internal use): 10
 The Role: Senior Data Engineer
 
The Team: The Market Intelligence Industry Data Solutions business line provides data technology and services supporting acquisition, ingestion, content management, mastering and distribution to power our Financial Institution Group business and customer needs. Focus on platform scale to support business by following a common data lifecycle that accelerates business value. We provide essential intelligence for Financial Services, Real Estate and Insurance industries
 
The Impact: The FIG Data Engineering team will be responsible for implementing & maintaining services and/or tools to support existing feed systems which allows users to consume FIG datasets and make FIG data available to a data fabric for wider consumption and processing within the company.
 
What’s in it for you: Ability to work with global stakeholders and working on latest tools and technologies.
 
What you'll be working on: 
Port existing data pipelines and make data available to an internal data fabric.
 Build new data acquisition and transformation pipelines using big data and cloud technologies.
 Work with the broader technology team, including information architecture and data fabric teams to align pipelines with the lodestone initiative.
 
What We’re Looking For
 
Required Qualifications: 
BS in Computer Science or Engineering with at least 4 years of professional software work experience
 In-depth knowledge of Structured Query Language (SQL) including advanced SQL coding, relational database design, data warehousing
 Experience with Big Data platforms such as Apache and Apache
 Deep understanding of REST, good API design, and OOP principles
 Strong collaboration and teamwork skills with excellent written and verbal communications skills
 Self-starter and motivated with ability to work in a fast-paced software development environment.
 Agile experience highly desirable
 Proficiency in the development environment, including IDE, database server, GitHub, Continuous Integration, unit-testing tool, and defect management tools.
 Basic understanding on software development programming languages like Oracle PL/SQL.
 
Preferred Qualifications: 
Experience with object-oriented/object function scripting languages: Python, C#, Scala, etc.
 Experience in maintaining and developing software in production utilizing cloud-based tooling (GCP, Okta)
 Experience in BigQuery, Snowflake, Databricks will be a big plus.
 
What’s In It For You?
 
Our Mission: 
Advancing Essential Intelligence.
 
Our People: 
We're more than 35,000 strong worldwide—so we're able to understand nuances while having a broad perspective. Our team is driven by curiosity and a shared belief that Essential Intelligence can help build a more prosperous future for us all.From finding new ways to measure sustainability to analyzing energy transition across the supply chain to building workflow solutions that make it easy to tap into insight and apply it. We are changing the way people see things and empowering them to make an impact on the world we live in. We’re committed to a more equitable future and to helping our customers find new, sustainable ways of doing business. Join us and help create the critical insights that truly make a difference.
 
Our Values: 
Integrity, Discovery, Partnership
 
Throughout our history, the world's leading organizations have relied on us for the Essential Intelligence they need to make confident decisions about the road ahead. We start with a foundation of integrity in all we do, bring a spirit of discovery to our work, and collaborate in close partnership with each other and our customers to achieve shared goals.
 
Benefits: 
We take care of you, so you can take care of business. We care about our people. That’s why we provide everything you—and your career—need to thrive at S&P Global.
 
Our benefits include: 
Health & Wellness: Health care coverage designed for the mind and body.
 
Flexible Downtime: Generous time off helps keep you energized for your time on.
 
Continuous Learning: Access a wealth of resources to grow your career and learn valuable new skills.
 
Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs.
 
Family Friendly Perks: It’s not just about you. S&P Global has perks for your partners and little ones, too, with some best-in class benefits for families.
 
Beyond the Basics: From retail discounts to referral incentive awards—small perks can make a big difference.
 For more information on benefits by country visit: https://spgbenefits.com/benefit-summaries
 
Global Hiring and Opportunity at S&P Global: 
At S&P Global, we are committed to fostering a connected and engaged workplace where all individuals have access to opportunities based on their skills, experience, and contributions. Our hiring practices emphasize fairness, transparency, and merit, ensuring that we attract and retain top talent. By valuing different perspectives and promoting a culture of respect and collaboration, we drive innovation and power global markets.
 
Recruitment Fraud Alert: 
If you receive an email from a spglobalind.com domain or any other regionally based domains, it is a scam and should be reported to reportfraud@spglobal.com . S&P Global never requires any candidate to pay money for job applications, interviews, offer letters, “pre-employment training” or for equipment/delivery of equipment. Stay informed and protect yourself from recruitment fraud by reviewing our guidelines, fraudulent domains, and how to report suspicious activity here .
 
-----------------------------------------------------------
 
Equal Opportunity Employer
 S&P Global is an equal opportunity employer and all qualified candidates will receive consideration for employment without regard to race/ethnicity, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, military veteran status, unemployment status, or any other status protected by law. Only electronic job submissions will be considered for employment.
 
If you need an accommodation during the application process due to a disability, please send an email to: EEO.Compliance@spglobal.com and your request will be forwarded to the appropriate person.
 
US Candidates Only: The EEO is the Law Poster http://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf describes discrimination protections under federal law. Pay Transparency Nondiscrimination Provision - https://www.dol.gov/sites/dolgov/files/ofccp/pdf/pay-transp_%20English_formattedESQA508c.pdf
 
----------------------------------------------------------- 20 - Professional (EEO-2 Job Categories-United States of America), IFTECH202.1 - Middle Professional Tier I (EEO Job Group), SWP Priority – Ratings - (Strategic Workforce Planning)
 
Job ID: 324545
 Posted On: 2026-01-27
 Location: Islamabad, Pakistan

Senior Data Engineer

Rapid Labs

Senior Full Stack Machine Learning EngineerAbout Rapid LabsRapid Labs is a US based AI startup focused on building production ready, practical machine learning systems that solve real operational problems. We partner with businesses to design and deploy AI solutions that deliver measurable outcomes, not experiments or hype driven prototypes.Our culture values ownership, engineering excellence, and clear thinking. We work in fast moving, ambiguous environments where strong fundamentals and real world execution matter more than buzzwords.About the RoleWe are hiring a Senior Full Stack Machine Learning Engineer to own the design, development, and operation of production grade machine learning systems. This role is centered on applied and classical machine learning, not LLM only or prompt driven workflows.You will work close to the problem space, translating messy operational and business challenges into reliable ML systems that run in production. This is a hands on role for engineers who enjoy building, deploying, and improving real systems in the cloud and taking responsibility for outcomes.What You Will DoBuild and Own End to End ML Systems<ul><li>Own the full machine learning lifecycle from raw data to deployed, monitored, and continuously improving models</li><li>Design predictive, prescriptive, and optimization solutions for use cases such as manufacturing operations, predictive maintenance, quality analytics, and supply chain efficiency</li><li>Apply classical ML and statistical methods including time series forecasting, anomaly detection, regression, classification, clustering, probabilistic modeling, and optimization</li></ul>Engineer Strong Data and Feature Pipelines<ul><li>Build reliable ETL and ELT pipelines for batch and near real time ML workloads</li><li>Perform exploratory data analysis to understand data quality, distributions, bias, and signal</li><li>Design feature pipelines and feature stores that keep training and inference consistent</li><li>Integrate data from APIs, databases, and event streams with strong validation, schema control, and observability</li></ul>Ship and Operate Production ML<ul><li>Deploy models as scalable services using APIs, batch jobs, or streaming inference</li><li>Implement monitoring for model performance, drift, data quality, and system health</li><li>Work closely with platform and software teams to deliver secure, reliable, and cost efficient systems</li><li>Help define and enforce MLOps standards including CI CD, versioning, reproducibility, and rollback strategies</li></ul>Lead Technically and Collaborate Effectively<ul><li>Turn ambiguous business problems into clear ML problem statements, success metrics, and delivery plans</li><li>Make sound technical decisions on model selection, evaluation methods, and trade offs</li><li>Communicate results, risks, and limitations clearly to stakeholders using dashboards, visualizations, and documentation</li><li>Mentor junior engineers and raise the bar for ML engineering practices at Rapid Labs</li></ul>What We Are Looking ForMust Have<ul><li>Seven or more years of experience across machine learning engineering, data engineering, and production systems</li><li>At least five years of hands on experience delivering end to end machine learning solutions in production</li><li>Strong depth in classical machine learning and applied statistics beyond generative AI focused work</li><li>Solid understanding of evaluation techniques, validation strategies, bias variance trade offs, and failure modes</li><li>Advanced Python skills for production ML including clean code, testing, packaging, and performance tuning</li><li>Strong SQL skills and experience designing data models for analytics and ML</li><li>Three or more years of cloud experience with GCP preferred and AWS acceptable</li><li>Hands on experience with BigQuery, Cloud Storage, Dataflow or Apache Beam, Airflow or Cloud Composer, and containerized deployments using Docker and Kubernetes or managed equivalents</li><li>Experience operationalizing ML using tools such as MLflow, CI CD pipelines, workflow orchestration, and monitoring systems</li><li>Proven ability to monitor, debug, and improve models after deployment including drift detection and retraining</li><li>Strong problem solving skills with the ability to translate business needs into ML outcomes</li><li>Clear and confident communication skills with both technical and non-technical stakeholders</li><li>Experience working with cross functional teams including engineering, operations, and product</li></ul>Nice to Have<ul><li>Experience in manufacturing, industrial systems, or supply chain domains</li><li>Ownership of a technical roadmap or AI product lifecycle from planning to delivery</li><li>GCP or AI and ML related professional certifications</li><li>Background in consulting or client facing roles</li><li>Some exposure to GenAI or LLMs is welcome when combined with strong classical ML fundamentals, with LLMs used as supporting components rather than the core focus</li></ul>Salary &amp; Benefits<ul><li>Competitive salary and fringe benefits.</li><li>Paid Time off</li><li>Leaves encashment</li><li>EOBI</li><li>Professional Development</li><li>Career Advancement</li><li>Team Building Activities</li><li>Innovative Work Environment</li><li>Work-Life Balance</li><li>Company trips</li><li>Wellness Programs</li><li>Performance based promotion</li></ul>Location: Lahore Office, Islamabad Office, or Remote (Flexible)This role offers flexibility in how you work. Candidates may choose to join our Lahore office, work from our Islamabad office, or operate fully remote, depending on what works best for them.Office Hours: 05:00 PM to 02:00 AM (Monday to Friday) Job Type: Full-timeApplication Question(s):<ul><li>What is your current salary?</li><li>What is your expected salary?</li><li>What is the duration of your notice period in days?</li><li>Do you have 7+ years of experience as a Senior Full Stack Machine Learning Engineer?</li></ul>Experience:<ul><li>Senior Full Stack Machine Learning Engineer: 7 years (Preferred)</li></ul>Language:<ul><li>English fluently (Preferred)</li></ul>Work Location: In person

Senior Full Stack Machine Learning Engineer

Fusemachines

<div>
 
 About Fusemachines
 
 
 
 Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients&rsquo; AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.
 
 
 Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.
 
 
 
 Type: Full-time, Remote
 
 
 
 
 Role Overview
 
 
 
 We&rsquo;re hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You&rsquo;ll work across the ML lifecycle&mdash;from problem framing and data exploration to model development, evaluation, deployment, and monitoring&mdash;often in partnership with client stakeholders and internal delivery teams.
 
 
 You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Problem Framing &amp; Stakeholder Partnership
 </li>
 <ul>
 <li>
 Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.)
 </li>
 <li>
 Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability)
 </li>
 </ul>
 <li>
 Data Analysis &amp; Feature Engineering
 </li>
 <ul>
 <li>
 Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses
 </li>
 <li>
 Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices
 </li>
 <li>
 Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions
 </li>
 </ul>
 <li>
 Model Development (Core ML)
 </li>
 <ul>
 <li>
 Train and tune supervised learning models for tabular data (e.g., logistic/linear models, tree-based methods, gradient boosting such as XGBoost/LightGBM/CatBoost, and neural nets for structured data)
 </li>
 <li>
 Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation
 </li>
 <li>
 Build time series models (statistical and ML/DL approaches) and validate with proper backtesting
 </li>
 <li>
 Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness
 </li>
 <li>
 Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making
 </li>
 </ul>
 <li>
 Deep Learning
 </li>
 <ul>
 <li>
 Build and train deep learning models using PyTorch or TensorFlow/Keras
 </li>
 <li>
 Use best practices for training (regularization, calibration, class imbalance handling, reproducibility, sound train/val/test design)
 </li>
 </ul>
 <li>
 Evaluation, Explainability, and Iteration
 </li>
 <ul>
 <li>
 Choose appropriate metrics (AUC/F1/PR, RMSE/MAE/MAPE, calibration, lift, and business KPIs) and create evaluation reports
 </li>
 <li>
 Perform error analysis and interpretation (feature importance/SHAP, cohort slicing) and iterate based on evidence
 </li>
 </ul>
 <li>
 Productionization &amp; MLOps (Project-Dependent)
 </li>
 <ul>
 <li>
 Package models for deployment (batch scoring pipelines or real-time APIs) and collaborate with engineers on integration
 </li>
 <li>
 Implement practical MLOps: versioning, reproducible training, automated evaluation, monitoring for drift/performance, and retraining plans
 </li>
 </ul>
 <li>
 Documentation &amp; Communication
 </li>
 <ul>
 <li>
 Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders
 </li>
 <li>
 Create documentation and lightweight demos that make results actionable
 
 </li>
 </ul>
 </ul>
 Success in This Role Looks Like
 
 
 <ul>
 <li>
 You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency)
 </li>
 <li>
 Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring
 </li>
 <li>
 Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly
 
 
 </li>
 </ul>
 
 Required Qualifications
 
 
 
 <ul>
 <li>
 3&ndash;8 years of experience in data science, machine learning engineering, or applied ML (mid-to-senior)
 </li>
 <li>
 Strong Python skills for data analysis and modeling (pandas/numpy/scikit-learn or equivalent)
 </li>
 <li>
 Strong SQL skills (joins, window functions, aggregation, performance awareness)
 </li>
 <li>
 Solid foundation in statistics (hypothesis testing, uncertainty, bias/variance, sampling) and practical experimentation mindset
 </li>
 <li>
 Hands-on experience across multiple model types, including:
 </li>
 <ul>
 <li>
 Classification &amp; regression
 </li>
 <li>
 Time series forecasting
 </li>
 <li>
 Clustering/segmentation
 </li>
 </ul>
 <li>
 Experience with deep learning in PyTorch or TensorFlow/Keras
 </li>
 <li>
 Strong problem-solving skills: ability to work with ambiguous goals and messy data
 </li>
 <li>
 Clear communication skills and ability to translate analysis into decisions
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience with Databricks for applied ML (e.g., Spark, Delta Lake, MLflow, Databricks Jobs/Workflows)
 </li>
 <li>
 Experience deploying models to production (APIs, batch pipelines) and maintaining them over time (monitoring, retraining)
 </li>
 <li>
 Experience with orchestration tools (Airflow, Prefect, Dagster) and modern data stacks (Snowflake/BigQuery/Redshift/Databricks)
 </li>
 <li>
 Experience with cloud platforms (AWS/GCP/Azure/IBM) and containerization (Docker)
 </li>
 <li>
 Experience with responsible AI and governance best practices (privacy/PII handling, auditability, access controls)
 </li>
 <li>
 Consulting or client-facing delivery experience
 
 
 </li>
 </ul>
 
 Certifications (Strong Plus)
 
 
 
 Candidates with at least one relevant certification are especially encouraged to apply:
 
 
 <ul>
 <li>
 Cloud certifications: AWS, Google Cloud, Microsoft Azure, or IBM (data/AI/ML tracks)
 </li>
 <li>
 Databricks certifications (Data Scientist, Data Engineer, or related)
 
 
 </li>
 </ul>
 
 Nice-to-Have
 
 
 
 <ul>
 <li>
 Causal inference experience (e.g., quasi-experimental methods, propensity scores, uplift/heterogeneous treatment effects, experimentation beyond A/B tests)
 </li>
 <li>
 Agentic development experience: designing and evaluating agentic workflows (tool use, planning, memory/state, guardrails) and integrating them into products
 </li>
 <li>
 Deep familiarity with agentic coding tools and workflows for accelerated product development (e.g., AI-assisted IDEs, code agents, automated testing/refactoring, repo-aware assistants), including strong judgment on quality, security, and maintainability
 
 
 </li>
 </ul>
 
 Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.
 
 
 
 Powered by JazzHR
 
 
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</div>

Machine Learning Engineer / Data Scientist

Solochoicez

<div><div><div><div>Posted date</div> <div>28th January, 2026</div> <div>Last date to apply</div> <div>28th February, 2026</div>
</div> 
<div><div>Country</div> <div>Pakistan</div> <div>Locations</div> <div>Khyber Pakhtunkhwa
</div></div> 
<div><div>Category</div> <div>Data Science
</div></div> 
<div><div>Type</div> <div>Full Time</div></div></div></div><div></div>
 <div>Job Summary

We are seeking an experienced Data Scientist to join our analytics team. The ideal candidate will apply data science methodologies, build predictive models, and deliver actionable insights to support key business decisions, advance data driven solutions, and enhance the impact of the company&rsquo;s technology offerings.

Responsibilities
<ul><li>Collect, clean, and preprocess large, complex datasets from multiple sources.</li>
<li>Develop and deploy machine learning models and predictive analytics solutions aligned with business goals.</li>
<li>Design and implement statistical analysis frameworks and experimental models to measure trends and patterns.</li>
<li>Build dashboards, reports, and visualizations to communicate insights to business stakeholders.</li>
<li>Collaborate with cross‑functional teams including engineering, product, and consulting departments to operationalize analytics solutions.</li>
<li>Support cloud‑based data infrastructure and workflows (AWS, Azure, or Google Cloud).</li>
<li>Ensure data quality, governance, and documentation across analytics projects.</li>
</ul>Requirements
<ul><li>Minimum 4 years of professional experience as a Data Scientist or in a related analytics role.</li>
<li>16 years of formal education in Computer Science, IT, Software Engineering, or a related quantitative discipline.</li>
<li>Solid proficiency in Python and relevant data science libraries.</li>
<li>Experience with SQL and working with relational databases.</li>
<li>Practical experience with machine learning techniques, feature engineering, and model evaluation.</li>
<li>Strong visualization skills using tools like Tableau, Power BI, matplotlib, or similar.</li>
<li>Understanding of statistical analysis and hypothesis testing.</li>
<li>Ability to translate complex data insights into clear recommendations for technical and non‑technical stakeholders.</li>
<li>Effective communication, problem‑solving, and teamwork skills.</li>
<li>Experience with cloud platforms (AWS, Azure, or GCP) and scalable data pipelines is a plus.</li>
<li>Certifications in data science or machine learning</li></ul></div>

Data Scientist

Biocare Enterprises

<ul><li>BIOMEDICAL ENGINEER</li><li>SUPPLY CHAIN MANAGER</li><li>DATA ENTRY OPERATOR</li></ul>Job Type: Full-timeWork Location: Hybrid remote in Islamabad

Customer Information Data Entry Operator

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