Your First Data Science Job

From Learner to Hired

You have the skills, the projects and the interview preparation. This final lesson covers the last mile: making sure your portfolio is ready, knowing where to apply, running an effective job search, and negotiating your first offer.

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Step 1: Final Portfolio Review Checklist

Before you send a single application, check every item:

GitHub

• 3 to 5 pinned repositories, each with a results-first README
• At least two projects using real or Nigerian data
• At least one supervised ML project with honest evaluation (baseline, test set, relevant metrics)
• At least one deployed app or dashboard with a live link
• Notebooks rerun cleanly from top to bottom, with no error cells
• No API keys, passwords or private data committed

CV (one page for entry level)

• A headline that matches the role: "Data Analyst | SQL, Python, Power BI"
• A projects section above unrelated work experience if you are switching careers
• Bullet points with results: "Built a churn model that finds 2.6x more at-risk customers than random targeting"
• Links to GitHub, LinkedIn and your live app
• Keywords from the job description (SQL, Python, pandas, scikit-learn, Power BI), because many companies use applicant tracking systems (ATS) that scan for them

LinkedIn

• A professional photo and a headline that says what you do, not "Aspiring..."
• An About section with your focus and two or three project highlights
• A Featured section linking your best projects
• Regular posts: share one chart and one insight from each project

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Step 2: Where to Apply

In Nigeria

• LinkedIn Jobs: the main channel for fintechs, banks and multinationals. Turn on job alerts for "data analyst" and "data scientist" in Lagos, Abuja and remote
• Jobberman, MyJobMag and Hot Nigerian Jobs: broad Nigerian job boards
• Company career pages: Moniepoint, Kuda, Flutterwave, Paystack, Interswitch, MTN, Access Bank and others post roles directly
• Graduate trainee programmes: banks and the big consulting firms run structured entry programmes with analytics tracks
• Communities: Data Science Nigeria, 3MTT, CareerEx and tech community Slack and WhatsApp groups often share roles before they are widely advertised

Globally and remotely

• LinkedIn with the "Remote" filter
• Wellfound (formerly AngelList Talent) for startups
• We Work Remotely and Remote OK
• Talent platforms: Andela, Turing and Toptal match vetted developers and data professionals with global companies
• Kaggle and Zindi: a strong competition profile sometimes leads to direct outreach

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Step 3: Run a Focused Job Search

Treat the search like a project with a pipeline:

• Target 10 to 15 quality applications a week rather than 100 generic ones
• Tailor your CV headline and top bullets to each role
• Referrals are the strongest channel. Ask people in your network for a referral, and reach out to data team members on LinkedIn with a short, specific message
• Track everything in a spreadsheet: company, role, date, contact, stage, next step
• Expect rejection. Response rates of 5 to 15% on cold applications are normal, and it is a numbers game plus steady improvement

A short outreach message that works:

"Hi Tolu, I'm a data analyst who has been building fintech projects, most recently a churn model on simulated telecom data that finds 2.6x more at-risk customers than random targeting (link). I saw the Junior Data Analyst role at your company and would really value 10 minutes of your perspective on the team. Thank you!"

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Step 4: Understand the Pay Landscape

Salaries in Nigeria vary enormously by company type, and they change quickly with inflation and exchange rates. As a rough guide:

• Fintechs, multinationals and well-funded startups usually pay at the top of the local market
• Banks offer structured pay plus benefits (HMO, pension, allowances), and often a 13th-month salary
• Consulting and development organisations vary widely by firm and funding
• Remote roles for foreign companies pay in USD, GBP or EUR, typically well above local rates, but they are much more competitive

Do your own research before any negotiation: ask peers in data communities, check recent posts from people in similar roles, use Glassdoor and LinkedIn salary insights where available, and look at salary ranges published in job ads. Always compare the total package: base pay, bonus, HMO, pension, allowances, learning budget, equipment and remote-work support.

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Step 5: Negotiate Your Offer

Many first-time candidates accept the first number. Negotiating politely and professionally is normal and expected, and it rarely costs you an offer.

Principles:

1. Let them name a number first if you can. If asked early, give a researched range: "Based on my research for this role, I'm looking at N___ to N___ per month, depending on the full package."
2. Get the offer in writing before negotiating.
3. Anchor to value, not need: refer to your skills, your projects and the market rate, not your rent.
4. Negotiate the package, not only base pay: a sign-on bonus, an earlier review date, a learning budget, remote days or a laptop.
5. Be gracious and specific: "I'm excited about this role. Given my experience with SQL and the churn project I shared, would you be able to move the base to N___?"
6. Know your walk-away point before the conversation.

A common, low-risk ask for a first role: "Could we agree a formal salary review after six months, based on clear objectives?"

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Step 6: Your First 90 Days

• Days 1-30: learn the business, the data sources and the people. Ask lots of questions and document what you learn
• Days 31-60: deliver a small, useful quick win: a cleaned dataset, a dashboard, or an analysis someone asked for
• Days 61-90: take on a larger project, and share results proactively in simple business language

Keep a "brag document": a running list of your projects and their impact. It makes performance reviews, promotions and your next job search much easier.

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You Made It

You have covered the full data science journey: Python, data collection and cleaning, EDA, machine learning, real-world projects and getting hired. The field rewards people who keep building, keep learning and keep sharing their work. Pick your next project today.